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Adjusted model prediction to account for a single datafile. Adjusted unscaling data as well.

This commit is contained in:
Hunter Nichols 2021-05-24 12:53:51 -07:00
parent 53503f40d2
commit 1488b31dce
2 changed files with 19 additions and 24 deletions
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11
compiler/characterizer/analytical_util.py
View File

@ -293,16 +293,13 @@ def unscale_data(data, file_path, pos=None):
# Hard coded to only convert the last max/min (i.e. the label of the data)
if pos == None:
maxs,mins,avgs = [maxs[-1]],[mins[-1]],[avgs[-1]]
maxs,mins,avgs = maxs[-1],mins[-1],avgs[-1]
else:
maxs,mins,avgs = [maxs[pos]],[mins[pos]],[avgs[pos]]
maxs,mins,avgs = maxs[pos],mins[pos],avgs[pos]
unscaled_data = []
for data_row in data:
unscaled_row = []
for val, cur_max, cur_min in zip(data_row, maxs, mins):
unscaled_val = val*(cur_max-cur_min) + cur_min
unscaled_row.append(unscaled_val)
unscaled_data.append(unscaled_row)
unscaled_val = data_row*(maxs-mins) + mins
unscaled_data.append(unscaled_val)
return unscaled_data

32
compiler/characterizer/regression_model.py
View File

@ -81,10 +81,10 @@ class regression_model(simulation):
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['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]['delay_lh'].append(sram_vals['rise_delay'])
port_data[port]['delay_hl'].append(sram_vals['fall_delay'])
port_data[port]['slew_lh'].append(sram_vals['rise_slew'])
port_data[port]['slew_hl'].append(sram_vals['fall_slew'])
port_data[port]['write1_power'].append(sram_vals['write1_power'])
port_data[port]['write0_power'].append(sram_vals['write0_power'])
@ -100,13 +100,12 @@ class regression_model(simulation):
debug.info(1, '{}, {}, {}, {}, {}'.format(slew,
load,
port,
sram_vals['delay_lh'],
sram_vals['slew_lh']))
sram_vals['rise_delay'],
sram_vals['rise_slew']))
# Estimate the period as double the delay with margin
period_margin = 0.1
sram_data = {"min_period": sram_vals['delay_lh'] * 2,
"leakage_power": sram_vals["leakage_power"],
"sim_time":sram_vals["sim_time"]}
sram_data = {"min_period": sram_vals['rise_delay'] * 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))
@ -118,20 +117,19 @@ class regression_model(simulation):
Generate a model and prediction for LIB output
"""
#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])])
#Scaled the inputs using first data file as a reference
scaled_inputs = np.asarray([scale_input_datapoint(model_inputs, data_path)])
predictions = {}
for dname in data_paths.keys():
path = data_paths[dname]
out_pos = 0
for dname in self.output_names:
m = models[dname]
features, labels = get_scaled_data(path)
scaled_pred = self.model_prediction(m, scaled_inputs)
pred = unscale_data(scaled_pred.tolist(), path)
pred = unscale_data(scaled_pred.tolist(), data_path, pos=self.num_inputs+out_pos)
debug.info(2,"Unscaled Prediction = {}".format(pred))
predictions[dname] = pred[0][0]
predictions[dname] = pred[0]
out_pos+=1
return predictions
def train_models(self):