Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation
Authors: Chengting Yu, Lei Liu, Gaoang Wang, Erping Li, Aili Wang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on CIFAR-10, CIFAR-100, Image Net, and CIFAR10-DVS validate that our method achieves comparable performance to BPTT counterparts, and surpasses state-of-the-art efficient training techniques. |
| Researcher Affiliation | Academia | 1 College of Information Science and Electronic Engineering, Zhejiang University 2 ZJU-UIUC Institute, Zhejiang University |
| Pseudocode | Yes | The pseudocode for rate-based backpropagation, illustrating the implementations for both rate M and rate S, is provided in Algorithm 1. Algorithm 1: Single Training Iteration of the Rate-based Backpropagation |
| Open Source Code | Yes | Our code is available at https://github.com/Tab-ct/rate-based-backpropagation. |
| Open Datasets | Yes | In this section, we conduct experiments on CIFAR-10 [37], CIFAR-100 [37], Image Net [11], and CIFAR10-DVS [41] to evaluate the proposed training method. |
| Dataset Splits | Yes | CIFAR-10 includes 60,000 images across 10 classes, with 50,000 for training and 10,000 for testing, whereas CIFAR-100 is spread over 100 classes. [...] The Image Net-1K dataset [11] comprises 1,281,167 training images and 50,000 validation images distributed across 1,000 classes |
| Hardware Specification | Yes | The experiments on CIFAR-10, CIFAR-100, and CIFAR10-DVS datasets run on one NVIDIA Ge Force RTX 3090 GPU. For Image Net, distributed data parallel processing is utilized across eight NVIDIA Ge Force RTX 4090 GPUs. |
| Software Dependencies | No | We implement SNNs training on the Pytorch [53] and Spiking Jelly [19] frameworks. (No version numbers provided for PyTorch or Spiking Jelly.) |
| Experiment Setup | Yes | We set Vth = 1, λ = 0.2, and employ the sigmoid-based surrogate function [19] for LIF neurons. Detailed setups are provided in Appendix C. [...] Table 3: Training hyperparameters. CIFAR-10/CIFAR-100/Image Net/CIFAR10-DVS: Epoch 300/300/100/300, Learning rate 0.1/0.1/0.2/0.1, Batch size 128/128/512/128, Weight decay 5e-4/5e-4/2e-5/5e-4. |