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Changed most lists to dict to reduce hardcoded indices

This commit is contained in:
Hunter Nichols 2021-01-19 13:47:54 -08:00
parent 7259c197d8
commit 6d2a35e929
1 changed files with 39 additions and 26 deletions
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65
compiler/characterizer/linear_regression.py
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@ -25,9 +25,18 @@ data_fnames = ["rise_delay.csv",
"read1_power.csv",
"read0_power.csv",
"leakage_data.csv"]
# Positions must correspond to data_fname list
lib_dnames = ["delay_lh",
"delay_hl",
"slew_lh",
"slew_hl",
"write1_power",
"write0_power",
"read1_power",
"read0_power",
"leakage_power"]
data_dir = OPTS.openram_tech+relative_data_path
data_paths = [data_dir +'/'+fname for fname in data_fnames]
data_paths = {dname:data_dir +'/'+fname for dname, fname in zip(lib_dnames, data_fnames)}
class linear_regression(simulation):
@ -60,31 +69,30 @@ class linear_regression(simulation):
for slew in slews:
for load in loads:
# List returned with value order being delay, power, leakage, slew
# FIXME: make order less hard coded
sram_vals = self.get_predictions(model_inputs+[slew, load], models)
# Delay is only calculated on a single port and replicated for now.
for port in self.all_ports:
port_data[port]['delay_lh'].append(sram_vals[0][0][0])
port_data[port]['delay_hl'].append(sram_vals[1][0][0])
port_data[port]['slew_lh'].append(sram_vals[2][0][0])
port_data[port]['slew_hl'].append(sram_vals[3][0][0])
port_data[port]['delay_lh'].append(sram_vals['delay_lh'])
port_data[port]['delay_hl'].append(sram_vals['delay_hl'])
port_data[port]['slew_lh'].append(sram_vals['slew_lh'])
port_data[port]['slew_hl'].append(sram_vals['slew_hl'])
port_data[port]['write1_power'].append(sram_vals[4][0][0])
port_data[port]['write0_power'].append(sram_vals[5][0][0])
port_data[port]['read1_power'].append(sram_vals[6][0][0])
port_data[port]['read0_power'].append(sram_vals[7][0][0])
port_data[port]['write1_power'].append(sram_vals['write1_power'])
port_data[port]['write0_power'].append(sram_vals['write0_power'])
port_data[port]['read1_power'].append(sram_vals['read1_power'])
port_data[port]['read0_power'].append(sram_vals['read0_power'])
# Disabled power not modeled. Copied from other power predictions
port_data[port]['disabled_write1_power'].append(sram_vals[4][0][0])
port_data[port]['disabled_write0_power'].append(sram_vals[5][0][0])
port_data[port]['disabled_read1_power'].append(sram_vals[6][0][0])
port_data[port]['disabled_read0_power'].append(sram_vals[7][0][0])
port_data[port]['disabled_write1_power'].append(sram_vals['write1_power'])
port_data[port]['disabled_write0_power'].append(sram_vals['write0_power'])
port_data[port]['disabled_read1_power'].append(sram_vals['read1_power'])
port_data[port]['disabled_read0_power'].append(sram_vals['read0_power'])
# Estimate the period as double the delay with margin
period_margin = 0.1
sram_data = {"min_period": sram_vals[0][0][0] * 2,
"leakage_power": sram_vals[8][0][0]}
sram_data = {"min_period": sram_vals['delay_lh'] * 2,
"leakage_power": sram_vals["leakage_power"]}
debug.info(2, "SRAM Data:\n{}".format(sram_data))
debug.info(2, "Port Data:\n{}".format(port_data))
@ -95,27 +103,32 @@ class linear_regression(simulation):
"""
Generate a model and prediction for LIB output
"""
scaled_inputs = np.asarray([scale_input_datapoint(model_inputs, data_paths[0])])
#Scaled the inputs using first data file as a reference
data_name = lib_dnames[0]
scaled_inputs = np.asarray([scale_input_datapoint(model_inputs, data_paths[data_name])])
predictions = []
for m, path in zip(models, data_paths):
predictions = {}
for dname in data_paths.keys():
path = data_paths[dname]
m = models[dname]
features, labels = get_scaled_data(path)
scaled_pred = self.model_prediction(m, scaled_inputs)
pred = unscale_data(scaled_pred.tolist(), path)
debug.info(1,"Unscaled Prediction = {}".format(pred))
predictions.append(pred)
predictions[dname] = pred[0][0]
return predictions
def train_models(self):
"""
Generate and return models
"""
models = []
for path in data_paths:
features, labels = get_scaled_data(path)
models = {}
for dname, dpath in data_paths.items():
features, labels = get_scaled_data(dpath)
model = self.generate_model(features, labels)
models.append(model)
models[dname] = model
return models