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Early Termination Based Training Acceleration for an Energy-Efficient SNN Processor Design.

Sunghyun Choi, Dongwoo Lew, Jongsun Park

IEEE Transactions on Biomedical Circuits and Systems

|June 10, 2022

Summary

This summary is machine-generated.

We developed an early termination technique to speed up Spiking Neural Network (SNN) training. This method skips non-contributing images, significantly reducing training time and energy consumption for SNN processors.

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Area of Science:

Computer Science
Artificial Intelligence
Neuromorphic Engineering

Background:

Spiking Neural Networks (SNNs) offer energy-efficient computation but face training challenges.
Accelerating the training process of SNNs is crucial for their practical application.

Purpose of the Study:

To introduce a novel early termination technique for accelerating SNN processor training.
To reduce training time and energy consumption in SNNs without compromising accuracy.

Main Methods:

Developed an early termination scheme identifying non-contributing training images.
Implemented a metric to evaluate image contribution and an adaptive threshold for skipping computations.
Utilized a timestep splitting approach to enhance early termination frequency and savings.

Main Results:

Achieved significant reductions in synaptic operations (up to 93.53%) and feedforward timesteps (up to 90.82%) across multiple datasets.
Demonstrated substantial training energy savings (up to 61.76%) and computation cycle reduction (up to 69.10%) on hardware implementations.

Conclusions:

The proposed early termination and timestep splitting techniques effectively accelerate SNN training.
This approach offers a viable solution for energy-efficient and time-saving SNN processor design.