Image Classification on CIFAR-10 (Accuracy, Power, Delay)
90.6AccuracySCPU
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| SCPUFPGA model=Virtex-7, Frequency=200 MHz, SNN model=ResNet-11, Model depth=11, Precision=8 bits, LUTs=178k, FFs=127k2025.12 | 90.6 | 1.738 | 25.4 | |
| SiBrain (High Depth)FPGA model=Virtex-7, Frequency=200 MHz, SNN model=CONVNet(VGG-11), Model depth=11, Precision=8 bits, Number of parameters=9.2M, LUTs=140k, FFs=122k2025.12 | 90.25 | 1.555 | 18.9 | |
| residual SNN processorFPGA model=ZCU216, Frequency=100 MHz, SNN model=ResNet-10, Model depth=10, Precision=8 bits, Number of parameters=0.69M, LUTs=135k, FFs=342k2025.12 | 87.11 | 1.369 | 3.98 | |
| Aliyev et al.FPGA model=XCVU13P, Frequency=100 MHz, SNN model=VGG-9, Model depth=9, Precision=4 bits2025.12 | 86.6 | 0.73 | 59 | |
| SiBrainFPGA model=Virtex-7, Frequency=200 MHz, SNN model=CONVNet(VGG-11), Model depth=6, Precision=8 bits, Number of parameters=0.3M, LUTs=167k, FFs=136k2025.12 | 82.93 | 1.628 | 1.4 | |
| E3NEFPGA model=XCVU13P, Frequency=150 MHz, SNN model=AlexNet, Model depth=8, Precision=6 bits, LUTs=48k, FFs=50k2025.12 | 80.6 | 4.7 | 70 |