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Related Experiment Video

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High-accuracy deep ANN-to-SNN conversion using quantization-aware training framework and calcium-gated bipolar leaky

Haoran Gao1, Junxian He1, Haibing Wang1

  • 1The School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China.

Frontiers in Neuroscience
|March 27, 2023
PubMed
Summary

This study introduces a novel calcium-gated neuron model and a quantization-aware training framework for efficient Artificial Neural Network to Spiking Neural Network (ANN-to-SNN) conversion. The method achieves high accuracy with reduced inference latency, eliminating lengthy post-conversion steps.

Keywords:
ANN-to-SNN conversiondeep SNNsneuromorphic computingquantization-aware trainingspiking neural network

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Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Deep Learning

Background:

  • Spiking Neural Networks (SNNs) offer efficient event-driven computing.
  • Artificial Neural Network (ANN)-to-SNN conversion is a leading method for SNN accuracy.
  • Existing ANN-to-SNN methods require complex post-conversion steps and long inference times.

Purpose of the Study:

  • To develop an improved ANN-to-SNN conversion technique.
  • To reduce the discrepancy between ANN and SNN neuron behaviors.
  • To minimize inference latency in converted SNNs.

Main Methods:

  • Proposed a calcium-gated bipolar leaky integrate and fire (Ca-LIF) spiking neuron model.
  • Developed a quantization-aware training (QAT)-based framework for direct ANN-to-SNN weight export.
  • Utilized an off-the-shelf QAT toolkit for streamlined conversion.

Main Results:

  • The Ca-LIF model effectively approximates ReLU neuron functions.
  • The QAT framework enabled direct ANN-to-SNN conversion without post-processing.
  • Converted SNNs achieved competitive accuracy with significantly shorter inference time steps across various network structures.

Conclusions:

  • The proposed Ca-LIF neuron model and QAT framework offer an efficient and effective ANN-to-SNN conversion method.
  • This approach overcomes limitations of traditional ANN-to-SNN techniques, reducing complexity and inference latency.
  • The findings pave the way for more practical and performant SNN applications.