edwardzcl / ALL_INT_SNN

An All Integer-based Spiking Neural Network with Dynamic Threshold Adaptation

ALL_INT_SNN: An All Integer-based Spiking Neural Network with Dynamic Threshold Adaptation

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This code can be used as the supplemental material for the paper: "An All Integer-based Spiking Neural Network with Dynamic Threshold Adaptation". (Submitted to Frontiers in Neuroscience, June, 2024) .

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Citation:

To be completed.

Features:

• This supplemental material gives a reproduction function of ANN training, testing and converted SNN inference experiments in this paper.

File overview:

• README.md - this readme file.
  
• MNIST - the workspace folder for LeNet on MNIST.
  
• CIFAR10 - the workspace folder for VGG-Net on CIFAR10.
  

Requirements

Dependencies and Libraries:

• python 3.5 (https://www.python.org/ or https://www.anaconda.com/)
• tensorflow_gpu 1.2.1 (https://github.com/tensorflow)
• tensorlayer 1.8.5 (https://github.com/tensorlayer)
• CPU: Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
• GPU: Tesla V100

Installation:

To install requirements,

pip install -r requirements.txt

Datasets:

• MNIST: dataset, preprocessing
• CIFAR10: dataset, preprocessing

ANN Training

Before running:

• Please installing the required package Tensorflow and Tensorlayer (using our modified version)

• Please note your default dataset folder will be workspace/data, such as supplemental material/CIFAR10/data

• Select the index of GPU in the training scripts (0 by default)

Run the code:

for example (ANN training, k=0, VGG-Net, ternary weight, CIFAR10):

$ cd CIFAR10
$ python Quant_VGGNet_CIFAR10.py --k 0 --resume False --use_ternary True --learning_rate 0.001 --mode 'training'

ANN Inference

Run the code:

for example (ANN inference, k=0, VGG-Net, ternary weight, CIFAR10):

$ python Quant_VGGNet_CIFAR10.py --k 0 --resume True --use_ternary True --mode 'inference'

SNN inference

Run the code:

for example (SNN inference, k=0, noise_ratio=0, spiking VGG-Net, ternary weight, INT threshold, CIFAR10):

$ python Spiking_CNN1_CIFAR10.py --k 0 --use_ternary True --use_int True --noise_ratio 0

it will generate the corresponding log files including: accuracy.txt, sop_num.txt, spike_collect.txt, spike_num.txt, abs_spike.txt and sum_spike.txt in ./figs/WQ_INT/k0.

Others

• We do not consider the synaptic operations in the input encoding layer and the spike output in the last classification layer (membrane potential accumulation ) for both original ANN counterparts and converted SNNs.
  
• More instructions for running the code can be found from the arguments.

Results

Our proposed methods achieve the following performances on MNIST, and CIFAR10 dataset:

MNIST:

Quantization Level	Network Size	Epochs	ANN	SNN	Time Steps
Full-precision	32C5-P2-64C5-P2-1000	200	99.45%	N/A	N/A
k=0	32C5-P2-64C5-P2-1000	200	99.45%	99.45%	4

CIFAR10:

Quantization Level	Network Size	Epochs	ANN	SNN	Time Steps
Full-precision	96C3-256C3-P2-384C3-P2-384C3-256C3-P2-1024-1024	200	93.32%	N/A	N/A
k=0	96C3-256C3-P2-384C3-P2-384C3-256C3-P2-1024-1024	200	93.32%	93.15%	8

More question:

• There might be a little difference of results for multiple training repetitions, because of the randomization.
• Please feel free to reach out here or email: [e-mail], if you have any questions or difficulties. I'm happy to help guide you.