Pruning of Deep Spiking Neural Networks through Gradient Rewiring
Authors: Yanqi Chen, Zhaofei Yu, Wei Fang, Tiejun Huang, Yonghong Tian
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that the proposed method achieves minimal loss of SNNs performance on MNIST and CIFAR-10 datasets so far. |
| Researcher Affiliation | Academia | Yanqi Chen1,3 , Zhaofei Yu1,2,3 , Wei Fang1,3 , Tiejun Huang1,2,3 and Yonghong Tian1,3 1Department of Computer Science and Technology, Peking University 2Institute for Artificial Intelligence, Peking University 3Peng Cheng Laboratory |
| Pseudocode | Yes | Algorithm 1: Gradient Rewiring with SGD |
| Open Source Code | Yes | Our codes are available at https://github. com/Yanqi-Chen/Gradient-Rewiring. |
| Open Datasets | Yes | We evaluate the performance of the Grad R algorithm on image recognition benchmarks, namely the MNIST and CIFAR10 datasets. |
| Dataset Splits | No | The paper mentions training and test sets but does not explicitly provide details about a separate validation set split or its size/percentage. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Our implementation of deep SNN are based on our open-source SNN framework Spiking Jelly [Fang et al., 2020a]' and the use of 'Adam optimizer' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The choice of all hyperparameters is shown in Table 3 and Table 4. Table 3 lists 'N # Epoch 2048', 'Batch Size 16', 'T # Timestep 8', 'τm Membrane Constant 2.0', 'uth Firing Threshold 1.0', 'urest Resting Potential 0.0', 'p Target Sparsity 95%', 'η Learning Rate 1e-4', 'β1, β2 Adam Decay 0.9, 0.999' for CIFAR-10, and different values for MNIST. |