Newer
Older
#####################################################################################
################ Write C header file dedicated to the CNN params ####################
#####################################################################################
import sys, os
import math
import numpy as np
def create_C_header(filename,network_info,cim_dim,D_VEC,P_VEC,TIME_CONF,GAMMA_VEC,BETA_FP_VEC,GAMMA_FP_VEC):
# // Retrieve variables //
# CNN network info
Nlayers_cim = network_info[0];
Nlayers_fp = network_info[1];
Nimg = network_info[2];
# CIM dims
N_ROWS = cim_dim[0];
N_COLS = cim_dim[1];
# Channels
H_IMG = D_VEC[0];
W_IMG = D_VEC[1];
C_IN = D_VEC[2];
C_OUT = D_VEC[3];
# Precisions
R_IN = P_VEC[0];
R_W = P_VEC[1];
R_OUT = P_VEC[2];
R_BETA = P_VEC[3];
R_GAMMA = P_VEC[4];
# Timings
T_DP = TIME_CONF[0];
T_PRE = TIME_CONF[1];
T_MBIT = TIME_CONF[2];
T_ADC = TIME_CONF[3];
# // Reshape FP beta-offset
GAMMA_FP_VEC = np.reshape(GAMMA_FP_VEC,(Nlayers_fp,-1));
BETA_FP_VEC = np.reshape(BETA_FP_VEC,(Nlayers_fp,-1));
Nbeta_fp = np.shape(BETA_FP_VEC)[-1];
# // Write header file //
# Open file
fileID = open(filename,'w');
# Header
fileID.write('/*\n');
fileID.write(' *-----------------------------------\n');
fileID.write(' * Header file for CIM-QNN parameters\n');
fileID.write(' *-----------------------------------\n');
fileID.write('*/\n');
fileID.write('\n');
# Pre-processor statements
fileID.write(f'#define N_ROWS {N_ROWS}\n');
fileID.write(f'#define N_COLS {N_COLS}\n');
fileID.write('\n');
# Input img size
fileID.write('// Input img size\n');
fileID.write(f'uint8_t H_IMG = {H_IMG};\n')
fileID.write(f'uint8_t W_IMG = {W_IMG};\n')
# Layers & channels
fileID.write('// Networks channels\n');
fileID.write(f'uint16_t C_IN[{Nlayers_cim-START_LAYER}] = {{');
for i in range(len(C_IN)):
if(i == 0):
fileID.write(f'{C_IN[i]}');
else:
fileID.write(f',{C_IN[i]}');
fileID.write('}\n');
fileID.write(f'uint16_t C_OUT[{Nlayers_cim-START_LAYER}] = {{');
for i in range(len(C_OUT)):
if(i == 0):
fileID.write(f'{C_OUT[i]}');
else:
fileID.write(f',{C_OUT[i]}');
fileID.write('}\n');
fileID.write(f'uint8_t C_IN_LOG[{Nlayers_cim-START_LAYER}] = {{');
for i in range(len(C_IN)):
if(i == 0):
fileID.write(f'{int(math.log2(C_IN[i]))}');
else:
fileID.write(f',{int(math.log2(C_IN[i]))}');
fileID.write('}\n');
fileID.write(f'uint8_t C_OUT_LOG[{Nlayers_cim-START_LAYER}] = {{');
for i in range(len(C_OUT)):
if(i == 0):
fileID.write(f'{int(math.log2(C_OUT[i]))}');
else:
fileID.write(f',{int(math.log2(C_OUT[i]))}');
fileID.write('}\n');
# Precision
fileID.write('// Computing precision\n');
fileID.write(f'uint8_t R_IN = {R_IN}; uint8_t R_IN_LOG = {int(math.log2(R_IN))};\n');
fileID.write(f'uint8_t R_W = {R_W}; uint8_t R_W_LOG = {int(math.log2(R_W))};\n');
fileID.write(f'uint8_t R_OUT = {R_OUT}; uint8_t R_OUT_LOG = {int(math.log2(R_OUT))};\n');
fileID.write(f'uint8_t R_BETA = {R_BETA};\n');
fileID.write(f'uint8_t R_GAMMA = {R_GAMMA};\n');
fileID.write('\n');
# Timing configs
fileID.write('// Timing configuration\n');
fileID.write(f'uint8_t T_DP_CONF = {T_DP};\n');
fileID.write(f'uint8_t T_PRE_CONF = {T_PRE};\n');
fileID.write(f'uint8_t T_MBIT_CONF = {T_MBIT};\n');
fileID.write(f'uint8_t T_ADC_CONF = {T_ADC};\n');
fileID.write('\n');
fileID.write(f'uint8_t Nimg = {Nimg};\n');
fileID.write(f'uint8_t Nlayers_cim = {Nlayers_cim};\n');
fileID.write(f'uint8_t Nlayers_fp = {Nlayers_fp};\n');
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
fileID.write('\n');
# ABN params
fileID.write('// ABN CIM gain \n');
# Gain values
fileID.write(f'uint8_t GAMMA[{Nlayers_cim}] = {{');
for i in range(Nlayers_cim):
if(i==0):
fileID.write(f'{GAMMA_FP_VEC[i]}');
else:
fileID.write(f',{GAMMA_FP_VEC[i]}');
fileID.write(f'}};\n');
fileID.write('\n');
# ABN params
fileID.write('// ABN FP parameters\n');
# Gain values
fileID.write(f'uint32_t GAMMA_FP[{Nlayers_fp}] = {{');
for i in range(Nlayers_fp):
if(i==0):
fileID.write(f'{GAMMA_FP_VEC[i]}');
else:
fileID.write(f',{GAMMA_FP_VEC[i]}');
fileID.write(f'}};\n');
fileID.write('\n');
# Offsets value
fileID.write(f'uint32_t BETA_FP[{Nlayers_fp}][{Nbeta_fp}] = {{\n');
for i in range(Nlayers_fp):
fileID.write(f'{{');
for j in range(Nbeta_fp):
if(j==0):
fileID.write(f'{hex(BETA_FP_VEC[i][j])}');
else:
fileID.write(f',{hex(BETA_FP_VEC[i][j])}');
if(i==R_BETA-1):
fileID.write(f'}}\n');
else:
fileID.write(f'}},\n');
fileID.write(f'}};\n');
fileID.write('\n');
# Close file and return
fileID.close();
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
return;
def create_C_header_subset(filename,network_info,cim_dim,D_VEC,P_VEC,TIME_CONF,GAMMA_VEC,BETA_FP_VEC,GAMMA_FP_VEC,data_cim,START_LAYER):
# // Retrieve variables //
# CNN network info
Nlayers_cim = network_info[0];
Nlayers_fp = network_info[1];
Nimg = network_info[2];
# CIM dims
N_ROWS = cim_dim[0];
N_COLS = cim_dim[1];
# Channels
H_IMG = D_VEC[0];
W_IMG = D_VEC[1];
C_IN = D_VEC[2];
C_OUT = D_VEC[3];
# Precisions
R_IN = P_VEC[0];
R_W = P_VEC[1];
R_OUT = P_VEC[2];
R_BETA = P_VEC[3];
R_GAMMA = P_VEC[4];
# Timings
T_DP = TIME_CONF[0];
T_PRE = TIME_CONF[1];
T_MBIT = TIME_CONF[2];
T_ADC = TIME_CONF[3];
# CIM data, starting at the chosen layer index
data_in = data_cim[-1][START_LAYER-1];
data_w = data_cim[1];
data_b = data_cim[2];
data_w_fp = data_cim[3];
data_inf = data_cim[4];
# Reshape input data
data_in = np.reshape(data_in,(Nimg,-1));
# Reshape CIM offset
beta_conf_list = [];
for i in range(Nlayers_cim):
beta_conf_temp = np.expand_dims(data_b[i].astype("uint8"),axis=-1);
beta_unpacked = np.flip(np.unpackbits(beta_conf_temp,axis=-1),axis=-1);
# swap axes
beta_unpacked = np.swapaxes(beta_unpacked,0,1);
# Repeat beta values in r_w cols
beta_unpacked = np.repeat(beta_unpacked,R_W,axis=-1);
if(R_W*C_OUT[i] < 32):
beta_unpacked = np.pad(beta_unpacked,((0,0),(0,32-R_W*C_OUT[i])));
beta_conf_temp = np.dot(np.reshape(beta_unpacked[:R_BETA,...],(-1,32)),2**np.arange(32));
beta_conf_list.append(beta_conf_temp);
#Stack results along a single dimension
data_b = beta_conf_list;
# // Reshape FP beta-offset
GAMMA_FP_VEC = np.reshape(GAMMA_FP_VEC,(Nlayers_fp,-1));
BETA_FP_VEC = np.reshape(BETA_FP_VEC,(Nlayers_fp,-1));
Nbeta_fp = np.shape(BETA_FP_VEC)[-1];
# // Write header file //
# Open file
fileID = open(filename,'w');
# Header
fileID.write('/*\n');
fileID.write(' *-----------------------------------\n');
fileID.write(' * Header file for CIM-QNN parameters\n');
fileID.write(' *-----------------------------------\n');
fileID.write('*/\n');
fileID.write('\n');
# Pre-processor statements
fileID.write(f'#define N_ROWS {N_ROWS}\n');
fileID.write(f'#define N_COLS {N_COLS}\n');
fileID.write('\n');
# Input img size
fileID.write('// Input img size\n');
fileID.write(f'uint8_t H_IMG = {H_IMG};\n')
fileID.write(f'uint8_t W_IMG = {W_IMG};\n')
# Layers & channels
fileID.write('// Networks channels\n');
fileID.write(f'uint16_t C_IN[{Nlayers_cim-START_LAYER}] = {{');
for i in range(START_LAYER,len(C_IN)):
if(i == START_LAYER):
fileID.write(f'{C_IN[i]}');
else:
fileID.write(f',{C_IN[i]}');
fileID.write('};\n');
fileID.write(f'uint16_t C_OUT[{Nlayers_cim-START_LAYER}] = {{');
for i in range(START_LAYER,len(C_OUT)):
if(i == START_LAYER):
fileID.write(f'{C_OUT[i]}');
else:
fileID.write(f',{C_OUT[i]}');
fileID.write('};\n');
fileID.write(f'uint8_t C_IN_LOG[{Nlayers_cim-START_LAYER}] = {{');
for i in range(START_LAYER,len(C_IN)):
if(i == START_LAYER):
fileID.write(f'{int(math.log2(C_IN[i]))}');
else:
fileID.write(f',{int(math.log2(C_IN[i]))}');
fileID.write('};\n');
fileID.write(f'uint8_t C_OUT_LOG[{Nlayers_cim-START_LAYER}] = {{');
for i in range(START_LAYER,len(C_OUT)):
if(i == START_LAYER):
fileID.write(f'{int(math.log2(C_OUT[i]))}');
else:
fileID.write(f',{int(math.log2(C_OUT[i]))}');
fileID.write('};\n');
fileID.write('// FP channels \n');
fileID.write(f'uint16_t C_IN_FP[{Nlayers_fp}] = {{');
for i in range(Nlayers_fp):
if(i == 0):
fileID.write(f'{C_IN[Nlayers_cim-1+i]}');
else:
fileID.write(f',{C_IN[Nlayers_cim-1+i]}');
fileID.write('};\n');
fileID.write(f'uint16_t C_OUT_FP[{Nlayers_fp}] = {{');
for i in range(Nlayers_fp):
if(i == 0):
fileID.write(f'{C_OUT[Nlayers_cim-1+i]}');
else:
fileID.write(f',{C_OUT[Nlayers_cim-1+i]}');
fileID.write('};\n');
# Precision
fileID.write('// Computing precision\n');
fileID.write(f'uint8_t R_IN = {R_IN}; uint8_t R_IN_LOG = {int(math.log2(R_IN))};\n');
fileID.write(f'uint8_t R_W = {R_W}; uint8_t R_W_LOG = {int(math.log2(R_W))};\n');
fileID.write(f'uint8_t R_OUT = {R_OUT}; uint8_t R_OUT_LOG = {int(math.log2(R_OUT))};\n');
fileID.write(f'uint8_t R_BETA = {R_BETA};\n');
fileID.write(f'uint8_t R_GAMMA = {R_GAMMA};\n');
fileID.write('\n');
# Timing configs
fileID.write('// Timing configuration\n');
fileID.write(f'uint8_t T_DP_CONF = {T_DP};\n');
fileID.write(f'uint8_t T_PRE_CONF = {T_PRE};\n');
fileID.write(f'uint8_t T_MBIT_IN_CONF = {T_MBIT};\n');
fileID.write(f'uint8_t T_MBIT_W_CONF = {T_MBIT};\n');
fileID.write(f'uint8_t T_ADC_CONF = {T_ADC};\n');
fileID.write(f'uint8_t T_REF_CONF = {T_ADC};\n');
fileID.write('\n');
# Number of samples and layers
fileID.write(f'uint8_t Nimg = {Nimg};\n');
fileID.write(f'uint8_t Nlayers_cim = {Nlayers_cim-START_LAYER};\n');
fileID.write(f'uint8_t Nlayers_fp = {Nlayers_fp};\n');
fileID.write('\n');
# Inputs
fileID.write('// Input data \n');
fileID.write(f'uint32_t DATA_IN[{Nimg}][{np.shape(data_in)[1]}] = {{');
img_size = np.shape(data_in)[1];
for i in range(Nimg):
fileID.write('{');
for j in range(img_size):
if(j==img_size-1):
fileID.write(f'{data_in[i,j]}\n');
else:
fileID.write(f'{data_in[i,j]},\n');
if(i == Nimg-1):
fileID.write('}\n');
else:
fileID.write('},\n');
fileID.write(f'}};\n');
fileID.write('\n');
# Weights
fileID.write('// Weight data \n');
max_w = np.size(data_w[START_LAYER]); # ! Only valid for FC networks
fileID.write(f'uint32_t W_CIM[{Nlayers_cim-START_LAYER}][{max_w}] = {{');
for i in range(START_LAYER,Nlayers_cim):
fileID.write('{');
layer_size = np.size(data_w[i]);
for j in range(max_w):
if(j<layer_size):
if(j==max_w-1):
fileID.write(f'{data_w[i][j]}\n');
else:
fileID.write(f'{data_w[i][j]},\n');
else:
if(j==max_w-1):
fileID.write(f'0x0\n');
else:
fileID.write(f'0x0,\n');
if(i == Nlayers_cim-1):
fileID.write('}\n');
else:
fileID.write('},\n');
fileID.write(f'}};\n');
fileID.write('\n');
# ABN params
fileID.write('// ABN CIM gain \n');
# Gain values
fileID.write(f'uint8_t GAMMA[{Nlayers_cim-START_LAYER}] = {{');
for i in range(START_LAYER,Nlayers_cim):
if(i==START_LAYER):
fileID.write(f'{GAMMA_VEC[i]}');
else:
fileID.write(f',{GAMMA_VEC[i]}');
fileID.write(f'}};\n');
fileID.write('\n');
fileID.write('// ABN CIM offset \n');
max_b = np.size(data_b[START_LAYER]); # ! Only valid for FC networks
fileID.write(f'uint32_t B_CIM[{Nlayers_cim-START_LAYER}][{max_b}] = {{');
for i in range(START_LAYER,Nlayers_cim):
fileID.write('{');
layer_size = np.size(data_b[i]);
for j in range(max_b):
if(j<layer_size):
if(j==max_b-1):
fileID.write(f'{data_b[i][j]}\n');
else:
fileID.write(f'{data_b[i][j]},\n');
else:
if(j==max_b-1):
fileID.write(f'0x0\n');
else:
fileID.write(f'0x0,\n');
if(i == Nlayers_cim-1):
fileID.write('}\n');
else:
fileID.write('},\n');
fileID.write(f'}};\n');
fileID.write('\n');
fileID.write('// FP weights \n');
max_w = np.size(data_w_fp[0]); # ! Only valid for FC networks
fileID.write(f'uint32_t W_FP[{Nlayers_fp}][{max_w}] = {{');
for i in range(Nlayers_fp):
fileID.write('{');
layer_size = np.size(data_w_fp[i]);
for j in range(max_w):
if(j<layer_size):
if(j==max_w-1):
fileID.write(f'{data_w_fp[i][j]}\n');
else:
fileID.write(f'{data_w_fp[i][j]},\n');
else:
if(j==max_w-1):
fileID.write(f'0x0\n');
else:
fileID.write(f'0x0,\n');
if(i == Nlayers_fp-1):
fileID.write('}\n');
else:
fileID.write('},\n');
fileID.write(f'}};\n');
fileID.write('\n');
# ABN params
fileID.write('// ABN FP parameters\n');
# Gain values
fileID.write(f'uint32_t GAMMA_FP[{Nlayers_fp}][{Nbeta_fp}] = {{');
print(GAMMA_FP_VEC); print(BETA_FP_VEC)
for i in range(Nlayers_fp):
fileID.write(f'{{');
for j in range(Nbeta_fp):
if(j==0):
fileID.write(f'{hex(GAMMA_FP_VEC[i][j])}');
else:
fileID.write(f',{hex(GAMMA_FP_VEC[i][j])}');
if(i==Nlayers_fp-1):
fileID.write(f'}}\n');
else:
fileID.write(f'}},\n');
fileID.write(f'}};\n');
fileID.write('\n');
# Offsets value
fileID.write(f'uint32_t BETA_FP[{Nlayers_fp}][{Nbeta_fp}] = {{\n');
for i in range(Nlayers_fp):
fileID.write(f'{{');
for j in range(Nbeta_fp):
if(j==0):
fileID.write(f'{hex(BETA_FP_VEC[i][j])}');
else:
fileID.write(f',{hex(BETA_FP_VEC[i][j])}');
if(i==Nlayers_fp-1):
fileID.write(f'}}\n');
else:
fileID.write(f'}},\n');
fileID.write(f'}};\n');
fileID.write('\n');
# ABN params
fileID.write('// Inference results \n');
# Gain values
fileID.write(f'uint8_t inf_result[{Nimg}] = {{');
for i in range(Nimg):
if(i==0):
fileID.write(f'{data_inf[i]}');
else:
fileID.write(f',{data_inf[i]}');
fileID.write(f'}};\n');
fileID.write('\n');
# Close file and return
fileID.close();