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Efficient Spiking Neural Networks With Radix Encoding.

Zhehui Wang, Xiaozhe Gu, Rick Siow Mong Goh

IEEE Transactions on Neural Networks and Learning Systems

|August 15, 2022

Summary

This summary is machine-generated.

This study introduces radix-encoded Spiking Neural Networks (SNNs) that achieve high accuracy with ultrashort spike trains, significantly improving efficiency over traditional Artificial Neural Networks (ANNs). This breakthrough enhances SNNs

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Area of Science:

Artificial Intelligence
Computer Science
Neuroscience

Background:

Spiking Neural Networks (SNNs) offer superior latency and energy efficiency compared to Artificial Neural Networks (ANNs) due to their event-driven computation.
A key limitation of SNNs is the need for long spike trains (often >1000 time steps) to achieve high accuracy, which negates their computational efficiency.
This extended computation time increases operations and latency, diminishing the practical advantages of SNNs.

Purpose of the Study:

To propose a novel radix encoding technique for SNNs to enable ultrashort spike trains.
To develop a method for integrating radix encoding into the ANN-to-SNN conversion process for efficient training.
To demonstrate significant improvements in accuracy and efficiency using the proposed radix-encoded SNNs.

Main Methods:

Introduction of a radix encoding method to represent information within SNNs using significantly fewer time steps.
Development of a compatible ANN-to-SNN conversion strategy to facilitate the training of radix-encoded SNNs on existing platforms.
Experimental evaluation using the VGG-16 network on the CIFAR-10 dataset to compare performance against state-of-the-art methods.

Main Results:

Radix-encoded SNNs achieved high accuracy using fewer than six time steps, outperforming traditional SNNs.
The proposed method demonstrated a 25x improvement in latency compared to existing state-of-the-art approaches.
An accuracy improvement of 1.7% was observed on the CIFAR-10 dataset when using the VGG-16 architecture.

Conclusions:

Radix encoding offers a viable solution to overcome the latency and computational overhead limitations of traditional SNNs.
The developed ANN-to-SNN conversion method enables efficient training and deployment of these advanced SNNs.
This approach significantly enhances the practical applicability of SNNs for tasks requiring both high accuracy and efficiency.