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This study introduces temporal compression for spiking neural networks (SNNs) to enhance energy efficiency and speed. The new method significantly boosts throughput and reduces energy use in SNN hardware accelerators.

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input-output-weighted spiking neuronsliquid-state machinespiking neural networkstime averagingtime compression

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Neuromorphic Engineering

Background:

  • Spiking neural networks (SNNs) offer energy-efficient computation using rate and temporal coding.
  • Current SNN designs require extensive spike processing, limiting accuracy and efficiency.
  • Hardware accelerators for SNNs face challenges with switching power and limited throughput due to long spike trains.

Purpose of the Study:

  • To develop temporal compression techniques for SNN hardware accelerators.
  • To significantly improve throughput and reduce energy dissipation in SNNs.
  • To create a compression method applicable to various spike coding schemes and SNN designs.

Main Methods:

  • Proposed novel compression architectures including input spike compression units and weighted spiking neurons.
  • Implemented reconfigurable time constant scaling for flexible time compression ratios.
  • Integrated compression transparently into existing SNNs with minimal modifications to models and hardware.

Main Results:

  • Demonstrated feasibility of up to 16x time compression ratios in SNNs.
  • Achieved significant improvements: up to 15.93x in throughput, 13.88x in energy dissipation.
  • Showcased substantial gains in the trade-off between hardware area, runtime, energy, and accuracy on FPGA.

Conclusions:

  • Temporal compression offers a viable strategy to enhance SNN hardware accelerator performance.
  • The proposed method effectively boosts throughput and reduces energy consumption with minimal overhead.
  • This approach is broadly applicable to different SNNs and spike coding methods, paving the way for more efficient neuromorphic computing.