luke
/
OpenRAM
mirror of https://github.com/VLSIDA/OpenRAM.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Fixed issue with static inputs causing errors. Added corners to linear regression inputs.

This commit is contained in:
Hunter Nichols 2020-12-17 14:54:43 -08:00
parent b760656572
commit 240dc784af
2 changed files with 14 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

17
compiler/characterizer/analytical_util.py
View File

@ -91,7 +91,6 @@ def find_sample_position_with_min_error(data, sampled_vals):
return sample_pos return sample_pos
def squared_error(list_a, list_b): def squared_error(list_a, list_b):
#print('a:',list_a, 'b:', list_b)
error_sum = 0; error_sum = 0;
for a,b in zip(list_a, list_b): for a,b in zip(list_a, list_b):
error_sum+=(a-b)**2 error_sum+=(a-b)**2
@ -207,7 +206,6 @@ def sample_from_file(num_samples, file_name, sample_dir=None):
self_scaled_data[i].append((feature_list[i]-min_val)/(max_val-min_val)) self_scaled_data[i].append((feature_list[i]-min_val)/(max_val-min_val))
# Apply algorithm sampling points to available data # Apply algorithm sampling points to available data
sampled_data, unused_data = apply_samples_to_data(self_scaled_data,samples) sampled_data, unused_data = apply_samples_to_data(self_scaled_data,samples)
#print(sampled_data)
#unscale values and rescale using all available data (both sampled and unused points rescaled) #unscale values and rescale using all available data (both sampled and unused points rescaled)
if len(maxs)!=0 and len(mins)!=0: if len(maxs)!=0 and len(mins)!=0:
@ -229,10 +227,7 @@ def get_scaled_data(file_name):
# Data is scaled by max/min and data format is changed to points vs feature lists # Data is scaled by max/min and data format is changed to points vs feature lists
self_scaled_data = scale_data_and_transform(all_data) self_scaled_data = scale_data_and_transform(all_data)
print(self_scaled_data)
samples = np.asarray(self_scaled_data) samples = np.asarray(self_scaled_data)
print(samples)
features, labels = samples[:, :-1], samples[:,-1:] features, labels = samples[:, :-1], samples[:,-1:]
return features, labels return features, labels
@ -246,10 +241,10 @@ def scale_data_and_transform(data):
max_val = max(feature_list) max_val = max(feature_list)
min_val = min(feature_list) min_val = min(feature_list)
if max_val == min_val:
scaled_data[i] = [0.0 for _ in range(len(feature_list))]
else:
for i in range(len(feature_list)): for i in range(len(feature_list)):
if max_val == min_val:
scaled_data[i].append(0.0)
else:
scaled_data[i].append((feature_list[i]-min_val)/(max_val-min_val)) scaled_data[i].append((feature_list[i]-min_val)/(max_val-min_val))
return scaled_data return scaled_data
@ -265,6 +260,9 @@ def scale_input_datapoint(point, file_path):
scaled_point = [] scaled_point = []
for feature, mx, mn in zip(point, maxs, mins): for feature, mx, mn in zip(point, maxs, mins):
if mx == mn:
scaled_point.append(0.0)
else:
scaled_point.append((feature-mn)/(mx-mn)) scaled_point.append((feature-mn)/(mx-mn))
return scaled_point return scaled_point
@ -272,7 +270,7 @@ def unscale_data(data, file_path, pos=None):
if file_path: if file_path:
maxs,mins,avgs = get_max_min_from_file(file_path) maxs,mins,avgs = get_max_min_from_file(file_path)
else: else:
print("Must provide reference data to unscale") debug.error("Must provide reference data to unscale")
return None return None
# Hard coded to only convert the last max/min (i.e. the label of the data) # Hard coded to only convert the last max/min (i.e. the label of the data)
@ -294,7 +292,6 @@ def abs_error(labels, preds):
total_error = 0 total_error = 0
for label_i, pred_i in zip(labels, preds): for label_i, pred_i in zip(labels, preds):
cur_error = abs(label_i[0]-pred_i[0])/label_i[0] cur_error = abs(label_i[0]-pred_i[0])/label_i[0]
# print(cur_error)
total_error += cur_error total_error += cur_error
return total_error/len(labels) return total_error/len(labels)

5
compiler/characterizer/linear_regression.py
View File

@ -40,7 +40,10 @@ class linear_regression(simulation):
model_inputs = [log_num_words, model_inputs = [log_num_words,
OPTS.word_size, OPTS.word_size,
OPTS.words_per_row, OPTS.words_per_row,
self.sram.width * self.sram.height] self.sram.width * self.sram.height,
process_transform[self.process],
self.vdd_voltage,
self.temperature]
# List returned with value order being delay, power, leakage, slew # List returned with value order being delay, power, leakage, slew
# FIXME: make order less hard coded # FIXME: make order less hard coded