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Unsupervised Adaptive Weight Pruning for Energy-Efficient Neuromorphic Systems.

Wenzhe Guo1,2, Mohammed E Fouda3, Hasan Erdem Yantir1,2

  • 1Sensors Lab, Advanced Membranes & Porous Materials Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

Frontiers in Neuroscience
|December 7, 2020
PubMed
Summary

This study introduces an adaptive weight pruning method for spiking neural networks (SNNs). The technique significantly reduces computational load and energy use in neuromorphic systems with minimal accuracy loss.

Keywords:
STDPneuromorphic computingpattern recognitionpruningspiking neural networksunsupervised learning

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Area of Science:

  • Neuromorphic Engineering
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Deep neural networks demand substantial resources, limiting their use in energy-constrained neuromorphic hardware.
  • Spiking neural networks (SNNs) offer potential for energy efficiency but require optimization for complex tasks.

Purpose of the Study:

  • To develop an unsupervised, online adaptive weight pruning method for SNNs.
  • To enhance energy efficiency and reduce complexity in neuromorphic systems.

Main Methods:

  • Proposed an unsupervised online adaptive weight pruning technique for SNNs.
  • Dynamically adjusted pruning thresholds based on neural dynamics and firing activity.
  • Evaluated energy efficiency using synaptic operations (SOPs).

Main Results:

  • Achieved 30% reduction in SOPs during training and 55% during inference on MNIST with minimal accuracy loss (0.44%).
  • Demonstrated 3.33% higher accuracy and 67% greater SOP reduction compared to prior methods.
  • Confirmed effectiveness across various datasets and network sizes with negligible implementation overhead.

Conclusions:

  • The adaptive pruning method offers a promising solution for effective SNN compression.
  • Enables the development of highly energy-efficient neuromorphic systems for real-time applications.