The Hardware Impact of Quantization and Pruning for Weights in Spiking Neural Networks

Schaefer, Clemens JS; Taheri, Pooria; Horeni, Mark; Joshi, Siddharth

doi:10.1109/tcsii.2023.3260701

Cited by 8 publications

(1 citation statement)

References 17 publications

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“…SCNNs have achieved high precision with few timesteps (Chowdhury et al, 2021 ), with the spatio-temporal backpropagation (STBP) training method (Zhu and Shi, 2018 ), direct input encoding (Wu et al, 2019 ), and re-training strategy (Chowdhury et al, 2021 ). Second, a series of methods have been proposed to compact SCNNs, such as network pruning (Liu et al, 2022 ; Schaefer et al, 2023 ) to increase the sparsity and low-bit quantization (Kheradpisheh et al, 2022 ; Shymyrbay et al, 2022 ) to reduce the computational precision.…”

Section: Introductionmentioning

confidence: 99%

DT-SCNN: dual-threshold spiking convolutional neural network with fewer operations and memory access for edge applications

Lei,

Yang,

Liu

et al. 2024

Front. Comput. Neurosci.

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The spiking convolutional neural network (SCNN) is a kind of spiking neural network (SNN) with high accuracy for visual tasks and power efficiency on neuromorphic hardware, which is attractive for edge applications. However, it is challenging to implement SCNNs on resource-constrained edge devices because of the large number of convolutional operations and membrane potential (Vm) storage needed. Previous works have focused on timestep reduction, network pruning, and network quantization to realize SCNN implementation on edge devices. However, they overlooked similarities between spiking feature maps (SFmaps), which contain significant redundancy and cause unnecessary computation and storage. This work proposes a dual-threshold spiking convolutional neural network (DT-SCNN) to decrease the number of operations and memory access by utilizing similarities between SFmaps. The DT-SCNN employs dual firing thresholds to derive two similar SFmaps from one Vm map, reducing the number of convolutional operations and decreasing the volume of Vms and convolutional weights by half. We propose a variant spatio-temporal back propagation (STBP) training method with a two-stage strategy to train DT-SCNNs to decrease the inference timestep to 1. The experimental results show that the dual-thresholds mechanism achieves a 50% reduction in operations and data storage for the convolutional layers compared to conventional SCNNs while achieving not more than a 0.4% accuracy loss on the CIFAR10, MNIST, and Fashion MNIST datasets. Due to the lightweight network and single timestep inference, the DT-SCNN has the least number of operations compared to previous works, paving the way for low-latency and power-efficient edge applications.

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