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Sharing leaky-integrate-and-fire neurons for memory-efficient spiking neural networks.

Youngeun Kim1, Yuhang Li1, Abhishek Moitra1

  • 1Department of Electrical Engineering, Yale University, New Haven, CT, United States.

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|August 16, 2023
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Summary

EfficientLIF-Net reduces memory usage in Spiking Neural Networks (SNNs) by sharing Leaky-Integrate-and-Fire (LIF) neurons. This approach maintains accuracy while significantly improving memory efficiency for SNNs.

Keywords:
energy-efficient deep learningevent-based processingimage recognitionneuromorphic computingspiking neural network

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Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Neuroscience

Background:

  • Spiking Neural Networks (SNNs) offer energy-efficient computation through binary and asynchronous operations.
  • Leaky-Integrate-and-Fire (LIF) neurons, crucial for SNNs, require substantial memory to store membrane voltage for temporal dynamics.
  • Memory requirements for LIF neurons escalate with larger input dimensions, posing a challenge for SNN scalability.

Purpose of the Study:

  • To introduce a novel technique for reducing memory consumption in SNNs, specifically addressing the memory demands of LIF neurons.
  • To develop an efficient SNN architecture that maintains high accuracy while optimizing memory usage.

Main Methods:

  • Proposed EfficientLIF-Net, a novel SNN architecture that enables sharing of LIF neurons across different layers and channels.
  • Implemented and evaluated the EfficientLIF-Net on diverse benchmark datasets including CIFAR10, CIFAR100, TinyImageNet, ImageNet-100, and N-Caltech101.
  • Assessed the performance of EfficientLIF-Net on Human Activity Recognition (HAR) datasets, emphasizing its utility in temporal information processing.

Main Results:

  • Achieved comparable accuracy to standard SNNs.
  • Demonstrated significant memory efficiency gains: up to ~4.3x forward and ~21.9x backward memory efficiency for LIF neurons.
  • Validated the effectiveness of EfficientLIF-Net across various image classification and HAR tasks.

Conclusions:

  • EfficientLIF-Net presents a simple yet effective solution for enhancing memory efficiency in SNNs without compromising accuracy.
  • The proposed neuron-sharing strategy offers substantial memory savings, making SNNs more practical for large-scale applications.
  • EfficientLIF-Net shows promise for applications requiring efficient temporal information processing, such as HAR.