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Added updated model data with slews and loads. Changed linear regressions to account for additional models.

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Hunter Nichols
2021-01-13 13:04:34 -08:00
parent a802d2a0bd
commit ed3d39a1b8
11 changed files with 2000 additions and 51 deletions
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compiler/characterizer/lib.py
+2
View File
@@ -822,12 +822,14 @@ class lib:
write1_power = np.mean(self.char_port_results[port]["write1_power"])
write0_power = np.mean(self.char_port_results[port]["write0_power"])
datasheet.write("{0},{1},".format('write_rise_power_{}'.format(port), write1_power))
#FIXME: should be write_fall_power
datasheet.write("{0},{1},".format('read_fall_power_{}'.format(port), write0_power))
for port in self.read_ports:
read1_power = np.mean(self.char_port_results[port]["read1_power"])
read0_power = np.mean(self.char_port_results[port]["read0_power"])
datasheet.write("{0},{1},".format('read_rise_power_{}'.format(port), read1_power))
#FIXME: should be read_fall_power
datasheet.write("{0},{1},".format('write_fall_power_{}'.format(port), read0_power))
compiler/characterizer/linear_regression.py
+45 -26
View File
@@ -16,10 +16,15 @@ from sklearn.linear_model import LinearRegression
import math
relative_data_path = "/sim_data"
data_fnames = ["delay_data.csv",
"power_data.csv",
"leakage_data.csv",
"slew_data.csv"]
data_fnames = ["rise_delay.csv",
"fall_delay.csv",
"rise_slew.csv",
"fall_slew.csv",
"write1_power.csv",
"write0_power.csv",
"read1_power.csv",
"read0_power.csv",
"leakage_data.csv"]
data_dir = OPTS.openram_tech+relative_data_path
data_paths = [data_dir +'/'+fname for fname in data_fnames]
@@ -44,13 +49,9 @@ class linear_regression(simulation):
process_transform[self.process],
self.vdd_voltage,
self.temperature]
# List returned with value order being delay, power, leakage, slew
# FIXME: make order less hard coded
sram_vals = self.get_predictions(model_inputs)
self.create_measurement_names()
models = self.train_models()
# Set delay/power for slews and loads
port_data = self.get_empty_measure_data_dict()
@@ -58,32 +59,39 @@ class linear_regression(simulation):
max_delay = 0.0
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:
for mname in self.delay_meas_names + self.power_meas_names:
#FIXME: fix magic for indexing the data
#FIXME: model output is double list. Simply this
if "power" in mname:
port_data[port][mname].append(sram_vals[1][0][0])
elif "delay" in mname and port in self.read_ports:
port_data[port][mname].append(sram_vals[0][0][0])
elif "slew" in mname and port in self.read_ports:
port_data[port][mname].append(sram_vals[3][0][0])
else:
debug.error("Measurement name not recognized: {}".format(mname), 1)
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]['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])
# 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])
# 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[2][0][0]}
"leakage_power": sram_vals[8][0][0]}
debug.info(2, "SRAM Data:\n{}".format(sram_data))
debug.info(2, "Port Data:\n{}".format(port_data))
return (sram_data, port_data)
def get_predictions(self, model_inputs):
def get_predictions(self, model_inputs, models):
"""
Generate a model and prediction for LIB output
"""
@@ -91,15 +99,26 @@ class linear_regression(simulation):
scaled_inputs = np.asarray([scale_input_datapoint(model_inputs, data_paths[0])])
predictions = []
for path in data_paths:
for m, path in zip(models, data_paths):
features, labels = get_scaled_data(path)
model = self.generate_model(features, labels)
scaled_pred = self.model_prediction(model, scaled_inputs)
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)
return predictions
def train_models(self):
"""
Generate and return models
"""
models = []
for path in data_paths:
features, labels = get_scaled_data(path)
model = self.generate_model(features, labels)
models.append(model)
return models
def generate_model(self, features, labels):
"""
Supervised training of model.