This repository is the official implementation of BPLC+NOSO: Backpropagation of errors based on latency code with neurons that only spike once at the most. BPLC is backpropagation of errors based on latency code, which is mathematically rigorous given that no approximations of any gradient evaluations are used. When combined with neurons that spike once at the most (NOSOs), BPLC+NOSO highlights the following advantages of learning efficiency: (i) computational complexity for learning is independent of the input encoding length, and (ii) only few NOSOs are active during learning and inference periods, leading to large reduction in computational complexity.
To install requirements:
pip install -r requirements.txt
To train SNNs (FMNISTnet and CIFARnet) using BPLC on Fashion-MNIST or CIFAR-10, run this command:
cd BPLC+NOSO
python main.py --task <FMNIST or CIFAR10> --network <FMNISTnet or CIFARnet> --mode train
To evaluate SNNs(FMNISTnet and CIFARnet) on Fashion-MNIST or CIFAR-10, run this command:
cd BPLC+NOSO
python main.py --task <FMNIST or CIFAR10> --network <FMNISTnet or CIFARnet> --mode eval
Our model achieves the following performance on:
| Method | Network | Dataset | Accuracy (%) | # spikes (inference) |
|---|---|---|---|---|
| BPLC+NOSO | FMNISTnet | Fashion-MNIST | 92.47% | 14K ± 0.26K |
| BPLC+NOSO | CIFARnet | CIFAR-10 | 89.77% | 142K ± 1.86K |
*FMNISTnet : 32C5-P2-64C5-P2-600-10
*CIFARnet : 64C5-128C5-P2-256C5-P2-512C5-256C5-1024-512-10