In order to enable comparison, we recommend designs report benchmarking metrics for widely used state-of-the-art DNNs (e.g. AlexNet, VGG, GoogLeNet, ResNet) with input from well known datasets such as ImageNet. We aim to summarize the results on this website.
DNN models can be downloaded here.
Please submit benchmarking metrics using this form.
Name [Publication] | Process Technology | Power Supply Voltage | Clock Frequency (MHz) | Number of multipliers | Peak Performance (GMACs/sec) | Total Core area / Total number of multipliers (mm2) |
Total On-Chip memory / Total number of multipliers (kB) |
Measured or Simulated |
---|---|---|---|---|---|---|---|---|
Eyeriss [ISSCC 2016] |
65nm | 1.0 | 200 | 168 (16-bit) | 33.6 | 0.073 | 1.14 | Measured |
KU Leuven [VLSI 2016] |
40nm | 0.85 - 0.9 | 204 | 256 (16-bit) | 52.2 | 0.0094 | 0.58 | Measured |
Envision [ISSCC 2017] |
28nm | 0.65 - 1.0 | 200 | 256* (16-bit) *changes with bitwidth |
52.2 | 0.0074 | 0.58 | Measured |
EIE [ISCA 2016] |
45nm | 1.0 | 800 | 64 (16-bit) | 51.2 | 0.638 | 162 | Simulated (PnR) |
… | … | … | … | … | … | … | … | … |
Name [Publication] | Dense/Sparse | Supported Layers | Batch Size | Bits per Weight | Bits per Input Activation | Chip Power (mW) |
Chip Energy per non-zero MAC (pJ) |
Run Time (ms) |
Multiplier Utilization vs Peak (%) |
Off-chip accesses per non-zero MAC (Bytes) |
---|---|---|---|---|---|---|---|---|---|---|
Eyeriss [ISSCC 2016] |
Dense | CONV [all] | 4 | 16 | 16 | 278 | 21.7 | 115.3 | 41 | 0.010 |
KU Leuven [VLSI 2016] |
Dense [WACV 2016] |
CONV [all] | 1 | 7,7,8,9,9 | 4,7,9,8,8 | 78 | 10.7 | 21 | 14 | 0.066 |
Envision [ISSCC 2017] |
Dense [WACV 2016] |
CONV [all] | 1 | 7,7,8,9,9 | 4,7,9,8,8 | 44 | 6.0 | 21 | 14 | 0.055 |
EIE [ISCA 2016] |
Sparse [ICLR 2016] |
FC [all] | 1 | 16 | 16 | 579 | 14.5 | 0.05 | 76 | 0.009 |
… | … | … | … | … | … | … | … | … | … | … |
Name [Publication] | Dense/Sparse | Supported Layers | Batch Size | Bits per Weight | Bits per Input Activation | Chip Power (mW) |
Chip Energy per non-zero MAC (pJ) |
Run Time (ms) |
Multiplier Utilization vs Peak (%) |
Off-chip accesses per non-zero MAC (Bytes) |
---|---|---|---|---|---|---|---|---|---|---|
Eyeriss [ISSCC 2016] |
Dense | CONV [all] | 3 | 16 | 16 | 236 | 52.0 | 4309.4 | 13 | 0.016 |
Envision [ISSCC 2017] |
Dense [WACV 2016] |
CONV [all] | 1 | 5 | 4 (first), 6 (other layers) |
26 | 4.4 | 596.5 | 12 | 0.028 |
EIE [ISCA 2016] |
Sparse [ICLR 2016] |
FC [all] | 1 | 16 | 16 | 610 | 22.6 | 0.05 | 49 | 0.036 |
… | … | … | … | … | … | … | … | … | … | … |
Detailed summary of results here.