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CLIF: Complementary Leaky Integrate-and-Fire Neuron for Spiking Neural Networks (ICML 2024)

Commonly used LIF neurons suffer from the vanishing gradient problem in the temporal dimension. This work propose the Complementary Leaky Integrate-and-Fire (CLIF) Neuron without (hyper)parameters. CLIF creates extra paths to facilitate the backpropagation in computing temporal gradient while keeping binary output.

main_fig

It is feasible to train a model using CLIF and subsequently deploy it or inference with LIF. We take pre-trained models of CLIF and LIF (Resnet18 with T=6) to perform inference on the CIFAR10 and CIFAR100 tasks. To compensate for CLIF’s enhanced reset process, we employ a hard reset with a bias as a hyperparameter. As can be seen in Table, this approach leads to an inference accuracy that surpasses that of a model directly trained with LIF.

Table. Directly convert the pre-trained CLIF/LIF model to an LIF neuron for inference.
CIFAR10 Soft Reset Hard Reset
Reset Value None 0 -0.02 -0.04 -0.06 -0.08 -0.1
CLIF pretrained (95.41%) 92.95% 93.41% 94.18% 94.54% 95.08% 94.84% 94.72%
LIF pretrained (94.51%) 94.51% 84.05% 76.68% 66.08% 52.16% 38.00% 27.04%
CIFAR100 Soft Reset Hard Reset
Reset Value None 0 -0.02 -0.04 -0.06 -0.08 -0.1
CLIF pretrained (78.36%) 68.72% 73.04% 74.64% 76.63% 76.55% 77.00% 76.54%
LIF pretrained (76.23%) 76.23% 47.74% 37.04% 27.56% 19.83% 13.22% 8.77%

Dependencies

  • Python 3
  • PyTorch, torchvision
  • spikingjelly 0.0.0.0.12
  • Python packages: pip install tqdm progress torchtoolbox thop

Training

We use single GTX4090 GPU for running all the experiments. Multi-GPU training is not supported in the current codes.

Setup

CIFAR-10, CIFAR-100, Tiny-Imagenet, DVS-CIFAR10, and DVS-Gesture:

# CIFAR-10
python train_BPTT.py -data_dir ./data_dir -dataset cifar10 -model spiking_resnet18 -T_max 200 -epochs 200 -weight_decay 5e-5 -neuron CLIF

# CIFAR-100
python train_BPTT.py -data_dir ./data_dir -dataset cifar100 -model spiking_resnet18 -T_max 200 -epochs 200 -neuron CLIF

# Tiny-Imagenet
python train_BPTT.py -data_dir ./data_dir -dataset tiny_imagenet -model spiking_vgg13_bn -neuron CLIF
   
# DVS-CIFAR10
python train_BPTT.py -data_dir ./data_dir -dataset DVSCIFAR10 -T 10 -drop_rate 0.3 -model spiking_vgg11_bn -lr=0.05  -mse_n_reg -neuron CLIF

# DVS-Gesture
python train_BPTT.py -data_dir ./data_dir -dataset dvsgesture -model spiking_vgg11_bn -T 20 -b 16 -drop_rate 0.4  -neuron CLIF

If you change the neuron, you can directly switch to LIF or PLIF by modifying the hyperparameters after -neuron.

For example to setup LIF neuron for CIFAR-10 task:

# LIF neuron for CIFAR-10
python train_BPTT.py -data_dir ./data_dir -dataset cifar10 -model spiking_resnet18 -amp -T_max 200 -epochs 200 -weight_decay 5e-5 -neuron LIF

Inference

The inference setup could refer file: run_inference_script

Acknowlegement

The code for data preprocessing is based on the spikingjelly repo. This repository is mainly modified from this repo.