Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
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// -*- mode: C++; c-file-style: "cc-mode" -*-
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//*************************************************************************
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// DESCRIPTION: Verilator: Dataflow based optimization of combinational logic
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//
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// Code available from: https://verilator.org
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//
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//*************************************************************************
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//
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2025-01-01 14:30:25 +01:00
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// Copyright 2003-2025 by Wilson Snyder. This program is free software; you
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Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
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// can redistribute it and/or modify it under the terms of either the GNU
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// Lesser General Public License Version 3 or the Perl Artistic License
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// Version 2.0.
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// SPDX-License-Identifier: LGPL-3.0-only OR Artistic-2.0
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//
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//*************************************************************************
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//
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// High level entry points from Ast world to the DFG optimizer.
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//
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//*************************************************************************
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2023-10-18 12:37:46 +02:00
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#include "V3PchAstNoMT.h" // VL_MT_DISABLED_CODE_UNIT
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Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
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#include "V3DfgOptimizer.h"
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#include "V3AstUserAllocator.h"
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#include "V3Dfg.h"
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#include "V3DfgPasses.h"
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#include "V3Graph.h"
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#include "V3UniqueNames.h"
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#include <vector>
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VL_DEFINE_DEBUG_FUNCTIONS;
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// Extract more combinational logic equations from procedures for better optimization opportunities
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class DataflowExtractVisitor final : public VNVisitor {
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// NODE STATE
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// AstVar::user3 -> bool: Flag indicating variable is subject of force or release
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// statement AstVar::user4 -> bool: Flag indicating variable is combinationally driven
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// AstNodeModule::user4 -> Extraction candidates (via m_extractionCandidates)
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const VNUser3InUse m_user3InUse;
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const VNUser4InUse m_user4InUse;
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// Expressions considered for extraction as separate assignment to gain more opportunities for
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// optimization, together with the list of variables they read.
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2022-10-12 11:19:21 +02:00
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using Candidates = std::vector<std::pair<AstNodeExpr*, std::vector<const AstVar*>>>;
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
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// Expressions considered for extraction. All the candidates are pure expressions.
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AstUser4Allocator<AstNodeModule, Candidates> m_extractionCandidates;
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// STATE
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AstNodeModule* m_modp = nullptr; // The module being visited
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Candidates* m_candidatesp = nullptr;
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bool m_impure = false; // True if the visited tree has a side effect
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bool m_inForceReleaseLhs = false; // Iterating LHS of force/release
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// List of AstVar nodes read by the visited tree. 'vector' rather than 'set' as duplicates are
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// somewhat unlikely and we can handle them later.
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std::vector<const AstVar*> m_readVars;
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// METHODS
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// Node considered for extraction as a combinational equation. Trace variable usage/purity.
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void iterateExtractionCandidate(AstNode* nodep) {
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2022-10-12 11:19:21 +02:00
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UASSERT_OBJ(!VN_IS(nodep->backp(), NodeExpr), nodep,
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
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"Should not try to extract nested expressions (only root expressions)");
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// Simple VarRefs should not be extracted, as they only yield trivial assignments.
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// Similarly, don't extract anything if no candidate map is set up (for non-modules).
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// We still need to visit them though, to mark hierarchical references.
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if (VN_IS(nodep, NodeVarRef) || !m_candidatesp) {
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iterate(nodep);
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return;
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}
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// Don't extract plain constants
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if (VN_IS(nodep, Const)) return;
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// Candidates can't nest, so no need for VL_RESTORER, just initialize iteration state
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m_impure = false;
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m_readVars.clear();
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// Trace variable usage
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iterate(nodep);
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// We only extract pure expressions
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if (m_impure) return;
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// Do not extract expressions without any variable references
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if (m_readVars.empty()) return;
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// Add to candidate list
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2022-10-12 11:19:21 +02:00
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m_candidatesp->emplace_back(VN_AS(nodep, NodeExpr), std::move(m_readVars));
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
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}
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// VISIT methods
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void visit(AstNetlist* nodep) override {
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2023-11-11 05:25:53 +01:00
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// Analyze the whole design
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
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iterateChildrenConst(nodep);
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// Replace candidate expressions only reading combinationally driven signals with variables
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2022-10-21 13:05:38 +02:00
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V3UniqueNames names{"__VdfgExtracted"};
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
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for (AstNodeModule* modp = nodep->modulesp(); modp;
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modp = VN_AS(modp->nextp(), NodeModule)) {
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// Only extract from proper modules
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if (!VN_IS(modp, Module)) continue;
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for (const auto& pair : m_extractionCandidates(modp)) {
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2022-11-22 03:40:49 +01:00
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AstNodeExpr* const cnodep = pair.first;
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
|
|
|
|
|
// Do not extract expressions without any variable references
|
|
|
|
|
if (pair.second.empty()) continue;
|
|
|
|
|
|
|
|
|
|
// Check if all variables read by this expression are driven combinationally,
|
|
|
|
|
// and move on if not. Also don't extract it if one of the variables is subject
|
|
|
|
|
// to a force/release, as releasing nets must have immediate effect, but adding
|
|
|
|
|
// extra combinational logic can change semantics (see t_force_release_net*).
|
|
|
|
|
{
|
|
|
|
|
bool hasBadVar = false;
|
|
|
|
|
for (const AstVar* const readVarp : pair.second) {
|
|
|
|
|
// variable is target of force/release or not combinationally driven
|
|
|
|
|
if (readVarp->user3() || !readVarp->user4()) {
|
|
|
|
|
hasBadVar = true;
|
|
|
|
|
break;
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
if (hasBadVar) continue;
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// Create temporary variable
|
2022-11-22 03:40:49 +01:00
|
|
|
FileLine* const flp = cnodep->fileline();
|
|
|
|
|
const string name = names.get(cnodep);
|
|
|
|
|
AstVar* const varp = new AstVar{flp, VVarType::MODULETEMP, name, cnodep->dtypep()};
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
varp->trace(false);
|
|
|
|
|
modp->addStmtsp(varp);
|
|
|
|
|
|
|
|
|
|
// Replace expression with temporary variable
|
2022-11-22 03:40:49 +01:00
|
|
|
cnodep->replaceWith(new AstVarRef{flp, varp, VAccess::READ});
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
|
|
|
|
|
// Add assignment driving temporary variable
|
|
|
|
|
modp->addStmtsp(
|
2022-11-22 03:40:49 +01:00
|
|
|
new AstAssignW{flp, new AstVarRef{flp, varp, VAccess::WRITE}, cnodep});
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
void visit(AstNodeModule* nodep) override {
|
|
|
|
|
VL_RESTORER(m_modp);
|
|
|
|
|
m_modp = nodep;
|
|
|
|
|
iterateChildrenConst(nodep);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
void visit(AstAlways* nodep) override {
|
|
|
|
|
VL_RESTORER(m_candidatesp);
|
|
|
|
|
// Only extract from combinational logic under proper modules
|
2025-08-18 01:14:34 +02:00
|
|
|
const bool isComb = !nodep->sentreep()
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
&& (nodep->keyword() == VAlwaysKwd::ALWAYS
|
|
|
|
|
|| nodep->keyword() == VAlwaysKwd::ALWAYS_COMB
|
|
|
|
|
|| nodep->keyword() == VAlwaysKwd::ALWAYS_LATCH);
|
|
|
|
|
m_candidatesp
|
|
|
|
|
= isComb && VN_IS(m_modp, Module) ? &m_extractionCandidates(m_modp) : nullptr;
|
|
|
|
|
iterateChildrenConst(nodep);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
void visit(AstAssignW* nodep) override {
|
|
|
|
|
// Mark LHS variable as combinationally driven
|
2025-08-20 19:21:24 +02:00
|
|
|
if (const AstVarRef* const vrefp = VN_CAST(nodep->lhsp(), VarRef)) {
|
|
|
|
|
vrefp->varp()->user4(true);
|
|
|
|
|
}
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
//
|
|
|
|
|
iterateChildrenConst(nodep);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
void visit(AstAssign* nodep) override {
|
|
|
|
|
iterateExtractionCandidate(nodep->rhsp());
|
|
|
|
|
iterate(nodep->lhsp());
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
void visit(AstAssignDly* nodep) override {
|
|
|
|
|
iterateExtractionCandidate(nodep->rhsp());
|
|
|
|
|
iterate(nodep->lhsp());
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
void visit(AstIf* nodep) override {
|
|
|
|
|
iterateExtractionCandidate(nodep->condp());
|
|
|
|
|
iterateAndNextConstNull(nodep->thensp());
|
|
|
|
|
iterateAndNextConstNull(nodep->elsesp());
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
void visit(AstAssignForce* nodep) override {
|
2022-11-19 21:23:37 +01:00
|
|
|
VL_RESTORER(m_inForceReleaseLhs);
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
iterate(nodep->rhsp());
|
|
|
|
|
UASSERT_OBJ(!m_inForceReleaseLhs, nodep, "Should not nest");
|
|
|
|
|
m_inForceReleaseLhs = true;
|
|
|
|
|
iterate(nodep->lhsp());
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
void visit(AstRelease* nodep) override {
|
2022-11-19 21:23:37 +01:00
|
|
|
VL_RESTORER(m_inForceReleaseLhs);
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
UASSERT_OBJ(!m_inForceReleaseLhs, nodep, "Should not nest");
|
|
|
|
|
m_inForceReleaseLhs = true;
|
|
|
|
|
iterate(nodep->lhsp());
|
|
|
|
|
}
|
|
|
|
|
|
2022-10-12 11:19:21 +02:00
|
|
|
void visit(AstNodeExpr* nodep) override { iterateChildrenConst(nodep); }
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
|
|
|
|
|
void visit(AstNodeVarRef* nodep) override {
|
|
|
|
|
if (nodep->access().isWriteOrRW()) {
|
|
|
|
|
// If it writes a variable, mark as impure
|
|
|
|
|
m_impure = true;
|
|
|
|
|
// Mark target of force/release
|
|
|
|
|
if (m_inForceReleaseLhs) nodep->varp()->user3(true);
|
|
|
|
|
} else {
|
|
|
|
|
// Otherwise, add read reference
|
|
|
|
|
m_readVars.push_back(nodep->varp());
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
void visit(AstNode* nodep) override {
|
|
|
|
|
// Conservatively assume unhandled nodes are impure. This covers all AstNodeFTaskRef
|
2022-10-12 11:19:21 +02:00
|
|
|
// as AstNodeFTaskRef are sadly not AstNodeExpr.
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
m_impure = true;
|
|
|
|
|
// Still need to gather all references/force/release, etc.
|
|
|
|
|
iterateChildrenConst(nodep);
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// CONSTRUCTOR
|
|
|
|
|
explicit DataflowExtractVisitor(AstNetlist* netlistp) { iterate(netlistp); }
|
|
|
|
|
|
|
|
|
|
public:
|
|
|
|
|
static void apply(AstNetlist* netlistp) { DataflowExtractVisitor{netlistp}; }
|
|
|
|
|
};
|
|
|
|
|
|
|
|
|
|
void V3DfgOptimizer::extract(AstNetlist* netlistp) {
|
2025-05-23 02:29:32 +02:00
|
|
|
UINFO(2, __FUNCTION__ << ":");
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
// Extract more optimization candidates
|
|
|
|
|
DataflowExtractVisitor::apply(netlistp);
|
2024-01-09 16:35:13 +01:00
|
|
|
V3Global::dumpCheckGlobalTree("dfg-extract", 0, dumpTreeEitherLevel() >= 3);
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
}
|
|
|
|
|
|
2025-08-05 15:29:33 +02:00
|
|
|
class DataflowOptimize final {
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
// NODE STATE
|
2025-08-05 11:24:54 +02:00
|
|
|
// AstVar::user1, AstVarScope::user1 -> int, used as a bit-field
|
|
|
|
|
// - bit0: Read via AstVarXRef (hierarchical reference)
|
|
|
|
|
// - bit1: Written via AstVarXRef (hierarchical reference)
|
|
|
|
|
// - bit2: Read by logic in same module/netlist not represented in DFG
|
|
|
|
|
// - bit3: Written by logic in same module/netlist not represented in DFG
|
2025-08-05 13:11:02 +02:00
|
|
|
// - bit31-4: Reference count, how many DfgVertexVar represent this variable
|
2025-08-05 11:24:54 +02:00
|
|
|
//
|
|
|
|
|
// AstNode::user2/user3/user4 can be used by various DFG algorithms
|
2025-08-05 15:29:33 +02:00
|
|
|
const VNUser1InUse m_user1InUse;
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
|
2025-08-05 15:29:33 +02:00
|
|
|
// STATE
|
|
|
|
|
V3DfgContext m_ctx; // The context holding values that need to persist across multiple graphs
|
|
|
|
|
|
|
|
|
|
void optimize(DfgGraph& dfg) {
|
Optimize complex combinational logic in DFG (#6298)
This patch adds DfgLogic, which is a vertex that represents a whole,
arbitrarily complex combinational AstAlways or AstAssignW in the
DfgGraph.
Implementing this requires computing the variables live at entry to the
AstAlways (variables read by the block), so there is a new
ControlFlowGraph data structure and a classical data-flow analysis based
live variable analysis to do that at the variable level (as opposed to
bit/element level).
The actual CFG construction and live variable analysis is best effort,
and might fail for currently unhandled constructs or data types. This
can be extended later.
V3DfgAstToDfg is changed to convert the Ast into an initial DfgGraph
containing only DfgLogic, DfgVertexSplice and DfgVertexVar vertices.
The DfgLogic are then subsequently synthesized into primitive operations
by the new V3DfgSynthesize pass, which is a combination of the old
V3DfgAstToDfg conversion and new code to handle AstAlways blocks with
complex flow control.
V3DfgSynthesize by default will synthesize roughly the same constructs
as V3DfgAstToDfg used to handle before, plus any logic that is part of a
combinational cycle within the DfgGraph. This enables breaking up these
cycles, for which there are extensions to V3DfgBreakCycles in this patch
as well. V3DfgSynthesize will then delete all non synthesized or non
synthesizable DfgLogic vertices and the rest of the Dfg pipeline is
identical, with minor changes to adjust for the changed representation.
Because with this change we can now eliminate many more UNOPTFLAT, DFG
has been disabled in all the tests that specifically target testing the
scheduling and reporting of circular combinational logic.
2025-08-19 16:06:38 +02:00
|
|
|
if (dumpDfgLevel() >= 8) dfg.dumpDotFilePrefixed(m_ctx.prefix() + "dfg-in");
|
|
|
|
|
|
|
|
|
|
// Synthesize DfgLogic vertices
|
|
|
|
|
V3DfgPasses::synthesize(dfg, m_ctx);
|
|
|
|
|
if (dumpDfgLevel() >= 8) dfg.dumpDotFilePrefixed(m_ctx.prefix() + "synth");
|
2025-08-05 15:29:33 +02:00
|
|
|
|
|
|
|
|
// Extract the cyclic sub-graphs. We do this because a lot of the optimizations assume a
|
|
|
|
|
// DAG, and large, mostly acyclic graphs could not be optimized due to the presence of
|
|
|
|
|
// small cycles.
|
|
|
|
|
std::vector<std::unique_ptr<DfgGraph>> cyclicComponents
|
|
|
|
|
= dfg.extractCyclicComponents("cyclic");
|
|
|
|
|
|
|
|
|
|
// Attempt to convert cyclic components into acyclic ones
|
|
|
|
|
std::vector<std::unique_ptr<DfgGraph>> madeAcyclicComponents;
|
2025-08-08 23:53:12 +02:00
|
|
|
if (v3Global.opt.fDfgBreakCycles()) {
|
2025-08-05 15:29:33 +02:00
|
|
|
for (auto it = cyclicComponents.begin(); it != cyclicComponents.end();) {
|
|
|
|
|
auto result = V3DfgPasses::breakCycles(**it, m_ctx);
|
|
|
|
|
if (!result.first) {
|
|
|
|
|
// No improvement, moving on.
|
|
|
|
|
++it;
|
|
|
|
|
} else if (!result.second) {
|
|
|
|
|
// Improved, but still cyclic. Replace the original cyclic component.
|
|
|
|
|
*it = std::move(result.first);
|
|
|
|
|
++it;
|
|
|
|
|
} else {
|
|
|
|
|
// Result became acyclic. Move to madeAcyclicComponents, delete original.
|
|
|
|
|
madeAcyclicComponents.emplace_back(std::move(result.first));
|
|
|
|
|
it = cyclicComponents.erase(it);
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
}
|
|
|
|
|
// Merge those that were made acyclic back to the graph, this enables optimizing more
|
|
|
|
|
dfg.mergeGraphs(std::move(madeAcyclicComponents));
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
|
2025-08-05 15:29:33 +02:00
|
|
|
// Split the acyclic DFG into [weakly] connected components
|
|
|
|
|
std::vector<std::unique_ptr<DfgGraph>> acyclicComponents
|
|
|
|
|
= dfg.splitIntoComponents("acyclic");
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
|
2025-08-05 15:29:33 +02:00
|
|
|
// Quick sanity check
|
|
|
|
|
UASSERT(dfg.size() == 0, "DfgGraph should have become empty");
|
2022-09-28 15:42:18 +02:00
|
|
|
|
2025-08-05 15:29:33 +02:00
|
|
|
// For each acyclic component
|
|
|
|
|
for (const std::unique_ptr<DfgGraph>& component : acyclicComponents) {
|
|
|
|
|
// Optimize the component
|
|
|
|
|
V3DfgPasses::optimize(*component, m_ctx);
|
|
|
|
|
}
|
|
|
|
|
// Merge back under the main DFG (we will convert everything back in one go)
|
|
|
|
|
dfg.mergeGraphs(std::move(acyclicComponents));
|
|
|
|
|
|
|
|
|
|
// Eliminate redundant variables. Run this on the whole acyclic DFG. It needs to traverse
|
|
|
|
|
// the module/netlist to perform variable substitutions. Doing this by component would do
|
|
|
|
|
// redundant traversals and can be extremely slow when we have many components.
|
|
|
|
|
V3DfgPasses::eliminateVars(dfg, m_ctx.m_eliminateVarsContext);
|
|
|
|
|
|
|
|
|
|
// For each cyclic component
|
|
|
|
|
for (const std::unique_ptr<DfgGraph>& component : cyclicComponents) {
|
|
|
|
|
// Converting back to Ast assumes the 'regularize' pass was run, so we must run it
|
|
|
|
|
V3DfgPasses::regularize(*component, m_ctx.m_regularizeContext);
|
|
|
|
|
}
|
|
|
|
|
// Merge back under the main DFG (we will convert everything back in one go)
|
|
|
|
|
dfg.mergeGraphs(std::move(cyclicComponents));
|
2025-07-01 23:55:08 +02:00
|
|
|
|
Optimize complex combinational logic in DFG (#6298)
This patch adds DfgLogic, which is a vertex that represents a whole,
arbitrarily complex combinational AstAlways or AstAssignW in the
DfgGraph.
Implementing this requires computing the variables live at entry to the
AstAlways (variables read by the block), so there is a new
ControlFlowGraph data structure and a classical data-flow analysis based
live variable analysis to do that at the variable level (as opposed to
bit/element level).
The actual CFG construction and live variable analysis is best effort,
and might fail for currently unhandled constructs or data types. This
can be extended later.
V3DfgAstToDfg is changed to convert the Ast into an initial DfgGraph
containing only DfgLogic, DfgVertexSplice and DfgVertexVar vertices.
The DfgLogic are then subsequently synthesized into primitive operations
by the new V3DfgSynthesize pass, which is a combination of the old
V3DfgAstToDfg conversion and new code to handle AstAlways blocks with
complex flow control.
V3DfgSynthesize by default will synthesize roughly the same constructs
as V3DfgAstToDfg used to handle before, plus any logic that is part of a
combinational cycle within the DfgGraph. This enables breaking up these
cycles, for which there are extensions to V3DfgBreakCycles in this patch
as well. V3DfgSynthesize will then delete all non synthesized or non
synthesizable DfgLogic vertices and the rest of the Dfg pipeline is
identical, with minor changes to adjust for the changed representation.
Because with this change we can now eliminate many more UNOPTFLAT, DFG
has been disabled in all the tests that specifically target testing the
scheduling and reporting of circular combinational logic.
2025-08-19 16:06:38 +02:00
|
|
|
if (dumpDfgLevel() >= 8) dfg.dumpDotFilePrefixed(m_ctx.prefix() + "dfg-out");
|
2025-08-05 15:29:33 +02:00
|
|
|
}
|
2025-07-01 23:55:08 +02:00
|
|
|
|
2025-08-05 15:29:33 +02:00
|
|
|
DataflowOptimize(AstNetlist* netlistp, const string& label)
|
|
|
|
|
: m_ctx{label} {
|
2025-08-26 12:24:15 +02:00
|
|
|
|
|
|
|
|
// Mark interfaces that might be referenced by a virtual interface
|
|
|
|
|
if (v3Global.hasVirtIfaces()) {
|
|
|
|
|
netlistp->typeTablep()->foreach([](AstIfaceRefDType* nodep) {
|
|
|
|
|
if (!nodep->isVirtual()) return;
|
|
|
|
|
nodep->ifaceViaCellp()->setHasVirtualRef();
|
|
|
|
|
});
|
|
|
|
|
}
|
|
|
|
|
|
2025-08-05 15:29:33 +02:00
|
|
|
if (!netlistp->topScopep()) {
|
|
|
|
|
// Pre V3Scope application. Run on each module separately.
|
|
|
|
|
|
|
|
|
|
// Mark cross-referenced variables
|
|
|
|
|
netlistp->foreach([](const AstVarXRef* xrefp) {
|
|
|
|
|
AstVar* const tgtp = xrefp->varp();
|
|
|
|
|
if (xrefp->access().isReadOrRW()) DfgVertexVar::setHasRdXRefs(tgtp);
|
|
|
|
|
if (xrefp->access().isWriteOrRW()) DfgVertexVar::setHasWrXRefs(tgtp);
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
// Run the optimization
|
|
|
|
|
for (AstNode* nodep = netlistp->modulesp(); nodep; nodep = nodep->nextp()) {
|
|
|
|
|
// Only optimize proper modules
|
|
|
|
|
AstModule* const modp = VN_CAST(nodep, Module);
|
|
|
|
|
if (!modp) continue;
|
|
|
|
|
|
|
|
|
|
// Pre V3Scope application. Run on module.
|
|
|
|
|
UINFO(4, "Applying DFG optimization to module '" << modp->name() << "'");
|
|
|
|
|
++m_ctx.m_modules;
|
|
|
|
|
// Build the DFG of this module or netlist
|
2025-08-08 23:53:12 +02:00
|
|
|
const std::unique_ptr<DfgGraph> dfgp = V3DfgPasses::astToDfg(*modp, m_ctx);
|
2025-08-05 15:29:33 +02:00
|
|
|
// Actually process the graph
|
|
|
|
|
optimize(*dfgp);
|
|
|
|
|
// Convert back to Ast
|
|
|
|
|
V3DfgPasses::dfgToAst(*dfgp, m_ctx);
|
|
|
|
|
}
|
|
|
|
|
} else {
|
|
|
|
|
// Post V3Scope application. Run on whole netlist.
|
|
|
|
|
UINFO(4, "Applying DFG optimization to entire netlist");
|
|
|
|
|
// Build the DFG of the entire netlist
|
2025-08-08 23:53:12 +02:00
|
|
|
const std::unique_ptr<DfgGraph> dfgp = V3DfgPasses::astToDfg(*netlistp, m_ctx);
|
2025-07-01 23:55:08 +02:00
|
|
|
// Actually process the graph
|
2025-08-05 15:29:33 +02:00
|
|
|
optimize(*dfgp);
|
2025-07-01 23:55:08 +02:00
|
|
|
// Convert back to Ast
|
2025-08-05 15:29:33 +02:00
|
|
|
V3DfgPasses::dfgToAst(*dfgp, m_ctx);
|
2024-03-02 20:49:29 +01:00
|
|
|
}
|
2025-08-05 15:29:33 +02:00
|
|
|
}
|
2024-02-11 16:41:10 +01:00
|
|
|
|
2025-08-05 15:29:33 +02:00
|
|
|
public:
|
|
|
|
|
static void apply(AstNetlist* netlistp, const string& label) {
|
|
|
|
|
DataflowOptimize{netlistp, label};
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
}
|
2025-08-05 15:29:33 +02:00
|
|
|
};
|
2024-02-11 16:41:10 +01:00
|
|
|
|
2025-08-05 15:29:33 +02:00
|
|
|
void V3DfgOptimizer::optimize(AstNetlist* netlistp, const string& label) {
|
|
|
|
|
UINFO(2, __FUNCTION__ << ":");
|
|
|
|
|
DataflowOptimize::apply(netlistp, label);
|
2024-01-09 16:35:13 +01:00
|
|
|
V3Global::dumpCheckGlobalTree("dfg-optimize", 0, dumpTreeEitherLevel() >= 3);
|
Introduce DFG based combinational logic optimizer (#3527)
Added a new data-flow graph (DFG) based combinational logic optimizer.
The capabilities of this covers a combination of V3Const and V3Gate, but
is also more capable of transforming combinational logic into simplified
forms and more.
This entail adding a new internal representation, `DfgGraph`, and
appropriate `astToDfg` and `dfgToAst` conversion functions. The graph
represents some of the combinational equations (~continuous assignments)
in a module, and for the duration of the DFG passes, it takes over the
role of AstModule. A bulk of the Dfg vertices represent expressions.
These vertex classes, and the corresponding conversions to/from AST are
mostly auto-generated by astgen, together with a DfgVVisitor that can be
used for dynamic dispatch based on vertex (operation) types.
The resulting combinational logic graph (a `DfgGraph`) is then optimized
in various ways. Currently we perform common sub-expression elimination,
variable inlining, and some specific peephole optimizations, but there
is scope for more optimizations in the future using the same
representation. The optimizer is run directly before and after inlining.
The pre inline pass can operate on smaller graphs and hence converges
faster, but still has a chance of substantially reducing the size of the
logic on some designs, making inlining both faster and less memory
intensive. The post inline pass can then optimize across the inlined
module boundaries. No optimization is performed across a module
boundary.
For debugging purposes, each peephole optimization can be disabled
individually via the -fno-dfg-peepnole-<OPT> option, where <OPT> is one
of the optimizations listed in V3DfgPeephole.h, for example
-fno-dfg-peephole-remove-not-not.
The peephole patterns currently implemented were mostly picked based on
the design that inspired this work, and on that design the optimizations
yields ~30% single threaded speedup, and ~50% speedup on 4 threads. As
you can imagine not having to haul around redundant combinational
networks in the rest of the compilation pipeline also helps with memory
consumption, and up to 30% peak memory usage of Verilator was observed
on the same design.
Gains on other arbitrary designs are smaller (and can be improved by
analyzing those designs). For example OpenTitan gains between 1-15%
speedup depending on build type.
2022-09-23 17:46:22 +02:00
|
|
|
}
|