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Merge pull request #33 from deepika7497/training
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PR to merge changes for input sparsity for both digital and analog versions
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@Aayush-Ankit
Aayush-Ankit committed Jun 15, 2020
2 parents 4a5ab26 + af3516a commit 942cc37
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2 changes: 1 addition & 1 deletion .gitignore
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*.pyc
*.sw*

*.log
/test/testasm/
/test/traces/
/test/coreMvm_test/
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4 changes: 3 additions & 1 deletion how_to_run.md
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Expand Up @@ -82,7 +82,9 @@ cp -R <example> puma-simulator/test/testasm/

#### 6.1 - Setup config file :

Config file - ```puma-simulator/include/config.py```.
Use the appropriate config file from ```puma-simulator/include/example-configs/(config file name)```.
For example: for mlp use ```config-mlp.py```.
Copy the file to ```puma-simulator/include/``` and rename it to ```config.py```.

Update ```num_tile_compute``` in config file based on the number of tiles generated in your ```<example>``` model.

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11 changes: 10 additions & 1 deletion include/config.py
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Expand Up @@ -7,6 +7,13 @@
xbar_record = 1
inference = 1
training = not(inference)
sparse_opt = 1 # Flag for Sparsity optimisaton (Make it 0 for only dense computations)

## Variable to define the type of MVMU
# One of "Analog", "Digital_V1" or "Digital_V2"
# Digital_V1 has compressed inputs (Data+Offset style)
# Digital_V2 has uncompressed inputs (Skips computations for 0 activation)
MVMU_ver = "Analog"

## Operand precision (fixed point allowed only): num_bits = int_bits + frac_bits
num_bits = 16
Expand All @@ -30,7 +37,9 @@
data_width = num_bits # (in bits)
xbdata_width = data_width # (in bits)
instrn_width = 48 # (in bits)

# Input and Weight parameters
input_prec = 16
weight_width = 16
# Change here - Specify the IMA parameters here
xbar_bits = 2
num_matrix = 2 # each matrix is 1-fw logical xbar for inference and 1-fw, 1-bw, and 1 delta logical xbar for training. Each logical xbar for inference is 8-fw physical xbar and for training 8-fw, 8-bw and 16-delta physical xbars.
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58 changes: 42 additions & 16 deletions include/constants.py
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Expand Up @@ -2,6 +2,7 @@
## It also holds power, area and latency numbers of different component used in DPE design
import config as cfg
import math
import constants_digital as digi_param
# Limits the number of cycles an IMA runs in case it doesn't halt
infinity = 100000

Expand Down Expand Up @@ -145,42 +146,47 @@
'16': 1.67 * 10**(-7)}

# ADC - Discuss exact values with ISSAC authors
# ADC Values for including sparsity
adc_lat_dict = {'1' : 12.5,
'2' : 25,
'3' : 37.5,
'4' : 50,
'5' : 62.5,
'6' : 75,
'7' : 87.5,
'5' : 62.5,
'6' : 75,
'7' : 87.5,
'8' : 100,
'9' : 112.5,
'16': 200}

adc_pow_dyn_dict = {'1' : 0.225,
'2' : 0.45,
'3' : 0.675,
'4' : 0.9,
'5' : 1.125,
'6' : 1.35,
'7' : 1.575,
'5' : 1.125,
'6' : 1.35,
'7' : 1.575,
'8' : 1.8,
'9' : 2.025,
'16': 3.6}

adc_pow_leak_dict = {'1' : 0.025,
'2' : 0.05,
'3' : 0.075,
'4' : 0.1,
'5' : 0.125,
'6' : 0.150,
'7' : 0.175,
'5' : 0.125,
'6' : 0.15,
'7' : 0.175,
'8' : 0.2,
'9' : 0.225,
'16': 0.4}

adc_area_dict = {'1' : 0.0012,
'2' : 0.0012,
'3' : 0.0012,
'4' : 0.0012,
'5' : 0.0012,
'6' : 0.0012,
'7' : 0.0012,
'5' : 0.0012,
'6' : 0.0012,
'7' : 0.0012,
'8' : 0.0012,
'9' : 0.0012,
'16': 0.0012}
Expand Down Expand Up @@ -353,7 +359,12 @@

# Chosen latency based on config file - only for components whose latency is parameter dependent
#xbar_lat = xbar_lat_dict [str(cfg.xbar_bits)][str(cfg.xbar_size)]
xbar_ip_lat = xbar_ip_lat
xbar_ip_lat_dict = {'0':0, '90':0, '80':0, '70':0, '60':0, '50':0, '40':0, '30':0, '20':0, '10':0}
if cfg.MVMU_ver == "Analog":
for key, value in xbar_ip_lat_dict.items():
xbar_ip_lat_dict[key] = xbar_ip_lat
else:
xbar_ip_lat_dict = digi_param.Digital_xbar_lat_dict[cfg.MVMU_ver][str(cfg.xbar_size)]
xbar_op_lat = xbar_op_lat
xbar_rd_lat = xbar_rd_lat
xbar_wr_lat = xbar_wr_lat
Expand All @@ -365,8 +376,11 @@
instrnMem_lat = instrnMem_lat_dict[str(cfg.instrnMem_size)]
dataMem_lat = dataMem_lat_dict[str(cfg.dataMem_size)]

# Chosen area based on config file - only for components whose latency is parameter dependent
xbar_area = xbar_area_dict [str(cfg.xbar_bits)][str(cfg.xbar_size)]
# Chosen area based on config file - only for components whose area is parameter dependent
if cfg.MVMU_ver == "Analog":
xbar_area = xbar_area_dict[str(cfg.xbar_bits)][str(cfg.xbar_size)]
else:
xbar_area = digi_param.Digital_xbar_area_dict[cfg.MVMU_ver][str(cfg.xbar_size)]
dac_area = dac_area_dict [str(cfg.dac_res)]
adc_area = adc_area_dict [str(cfg.adc_res)]
xbar_inMem_area = xbar_inMem_area_dict[str(cfg.xbar_size)]
Expand All @@ -388,8 +402,20 @@
instrnMem_pow_dyn = instrnMem_pow_dyn_dict[str(cfg.instrnMem_size)] * math.sqrt(8) #area scaling for 8 bytes per instruction
dataMem_pow_dyn = dataMem_pow_dyn_dict[str(cfg.dataMem_size)]

# Energy
xbar_ip_energy_dict = {'0':0, '90':0, '80':0, '70':0, '60':0, '50':0, '40':0, '30':0, '20':0, '10':0}
if cfg.MVMU_ver == "Analog":
for key,value in xbar_ip_energy_dict.items():
xbar_ip_energy_dict[key] = xbar_ip_lat*xbar_ip_pow_dyn
else:
xbar_ip_energy_dict = digi_param.Digital_xbar_energy_dict[cfg.MVMU_ver][str(cfg.xbar_size)]
print('xbar_ip_energy_dict', xbar_ip_energy_dict)

# Chosen leak_power based on config file - only for components whose latency is parameter dependent
xbar_pow_leak = 0
if cfg.MVMU_ver == "Analog":
xbar_pow_leak = 0
else:
xbar_pow_leak = digi_param.Digital_xbar_pow_leak_dict[str(cfg.xbar_size)]
dac_pow_leak = dac_pow_leak_dict [str(cfg.dac_res)]
adc_pow_leak = adc_pow_leak_dict [str(cfg.adc_res)]
xbar_inMem_pow_leak = xbar_inMem_pow_leak_dict[str(cfg.xbar_size)]
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175 changes: 175 additions & 0 deletions include/constants_digital.py
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## This file contains the power, area and latency numbers of Digital MVMUs for two versions
Digital_xbar_lat_dict = {'Digital_V1': {'32': { '0': 130, # first indexed by version then by xbar_size and then by % sparsity
'10': 114, # For V1 it is (4n+2)*T and for V2 it is (3n+2+xbar_size)*T
'20': 102,
'30': 90,
'40': 78,
'50': 66,
'60': 50,
'70': 38,
'80': 26,
'90': 14},
'64': { '0' : 258,
'10': 230,
'20': 206,
'30': 178,
'40': 154,
'50': 130,
'60': 102,
'70': 78,
'80': 50,
'90': 26},
'128':{ '0' : 514,
'10': 462,
'20': 410,
'30': 358,
'40': 306,
'50': 258,
'60': 206,
'70': 154,
'80': 102,
'90': 50},
'256':{ '0' : 1026,
'10': 922,
'20': 818,
'30': 718,
'40': 614,
'50': 514,
'60': 410,
'70': 306,
'80': 206,
'90': 102}},
'Digital_V2': {'32' :{ '0' : 130,
'10': 118,
'20': 109,
'30': 100,
'40': 91,
'50': 82,
'60': 70,
'70': 61,
'80': 52,
'90': 43},
'64' :{ '0' : 258,
'10': 237,
'20': 219,
'30': 198,
'40': 180,
'50': 162,
'60': 141,
'70': 123,
'80': 102,
'90': 84},
'128':{ '0' : 514,
'10': 475,
'20': 436,
'30': 397,
'40': 358,
'50': 322,
'60': 283,
'70': 244,
'80': 205,
'90': 166},
'256':{ '0' : 1026,
'10': 948,
'20': 870,
'30': 795,
'40': 717,
'50': 642,
'60': 564,
'70': 486,
'80': 411,
'90': 333}}}

Digital_xbar_area_dict = {'Digital_V1': { '32' : 0.16977, # first indexed by version then by xbar_size
'64' : 0.27701,
'128': 1.74020,
'256': 7.29481},
'Digital_V2': { '32' : 0.16949,
'64' : 0.27645,
'128': 1.73908,
'256': 7.29257}}

Digital_xbar_energy_dict = {'Digital_V1':{'32':{'0' : 5261.43744, # first indexed by version then by xbar_size and then by % of non_0 values
'10': 4613.872832, # For V1 it is (4n+2)*T and for V2 it is (3n+2+xbar_size)*T
'20': 4128.199376, # in pJ
'30': 3642.52592,
'40': 3156.852464,
'50': 2671.179008,
'60': 2023.6144,
'70': 1537.940944,
'80': 1052.267488,
'90': 566.594032},
'64':{'0' : 20844.00864,
'10': 18581.86252,
'20': 16642.88014,
'30': 14380.73402,
'40': 12441.75163,
'50': 10502.76925,
'60': 8240.623131,
'70': 6301.640745,
'80': 4039.494628,
'90': 2100.512242},
'128':{ '0' : 83018.14464,
'10': 74619.39346,
'20': 66220.64228,
'30': 57821.8911,
'40': 49423.13992,
'50': 41670.44653,
'60': 33271.69535,
'70': 24872.94417,
'80': 16474.19299,
'90': 8075.441812},
'256':{ '0' : 331639.0958,
'10': 298022.5268,
'20': 264405.9578,
'30': 232082.3337,
'40': 198465.7647,
'50': 166142.1407,
'60': 132525.5717,
'70': 98909.00265,
'80': 66585.3786,
'90': 32968.80959}},
'Digital_V2':{'32':{'0' : 4466.744263,
'10': 4053.765767,
'20': 3744.031895,
'30': 3434.298023,
'40': 3124.564151,
'50': 2814.830279,
'60': 2401.851783,
'70': 2092.117911,
'80': 1782.384039,
'90': 1472.650167},
'64':{'0' : 17654.27322,
'10': 16216.06481,
'20': 14983.31474,
'30': 13545.10633,
'40': 12312.35626,
'50': 11079.6062,
'60': 9641.397787,
'70': 8408.647721,
'80': 6970.439311,
'90': 5737.689245},
'128':{ '0' : 70237.24474,
'10': 64904.19392,
'20': 59571.14309,
'30': 54238.09226,
'40': 48905.04144,
'50': 43982.22529,
'60': 38649.17446,
'70': 33316.12363,
'80': 27983.07281,
'90': 22650.02198},
'256':{ '0' : 280471.5471,
'10': 259128.5,
'20': 237785.453,
'30': 217263.2925,
'40': 195920.2454,
'50': 175398.0849,
'60': 154055.0379,
'70': 132711.9909,
'80': 112189.8303,
'90': 90846.78326}}}
Digital_xbar_pow_leak_dict = { '32' :5.575928889, #in mW
'64' :12.82466678,
'128':40.24037556,
'256':120.2098611}
7 changes: 7 additions & 0 deletions include/example-configs/config-cnn.py
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Expand Up @@ -7,6 +7,13 @@
xbar_record = 1
inference = 1
training = not(inference)
sparse_opt = 1 # Flag for Sparsity optimisaton (Make it 0 for only dense computations)

## Variable to define the type of MVMU
# One of "Analog", "Digital_V1" or "Digital_V2"
# Digital_V1 has compressed inputs (Data+Offset style)
# Digital_V2 has uncompressed inputs (Skips computations for 0 activation)
MVMU_ver = "Digital_V2"

## Operand precision (fixed point allowed only): num_bits = int_bits + frac_bits
num_bits = 16
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7 changes: 7 additions & 0 deletions include/example-configs/config-mlp.py
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Expand Up @@ -7,6 +7,13 @@
xbar_record = 1
inference = 1
training = not(inference)
sparse_opt = 1 # Flag for Sparsity optimisaton (Make it 0 for only dense computations)

## Variable to define the type of MVMU
# One of "Analog", "Digital_V1" or "Digital_V2"
# Digital_V1 has compressed inputs (Data+Offset style)
# Digital_V2 has uncompressed inputs (Skips computations for 0 activation)
MVMU_ver = "Digital_V2"

## Operand precision (fixed point allowed only): num_bits = int_bits + frac_bits
num_bits = 16
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