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SNN4Agents: a framework for developing energy-efficient embodied spiking neural networks for autonomous agents.

Rachmad Vidya Wicaksana Putra1, Alberto Marchisio1, Muhammad Shafique1

  • 1eBrain Lab, Division of Engineering, New York University (NYU) Abu Dhabi, Abu Dhabi, United Arab Emirates.

Frontiers in Robotics and AI
|August 12, 2024
PubMed
Summary

We introduce SNN4Agents, a framework optimizing Spiking Neural Networks (SNNs) for energy-efficient autonomous agents. This approach significantly reduces memory, speeds up processing, and improves energy efficiency while maintaining high accuracy for embodied AI applications.

Keywords:
automotive dataautonomous agentsenergy efficiencyneuromorphic computingneuromorphic processorspiking neural networks

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Area of Science:

  • Robotics and Artificial Intelligence
  • Neuromorphic Engineering
  • Computer Vision

Background:

  • Autonomous agents like vehicles and robots require low power for extended operation.
  • Spiking Neural Networks (SNNs) offer efficient, bio-inspired computation for these agents.
  • Systematic optimization for embodied SNNs in autonomous systems is underdeveloped.

Purpose of the Study:

  • To propose SNN4Agents, a novel framework for energy-efficient embodied SNNs.
  • To define optimization stages for deploying SNNs in autonomous agents.
  • To enhance the performance of autonomous agents through optimized SNNs.

Main Methods:

  • Developed the SNN4Agents framework with weight quantization, timestep reduction, and attention window reduction.
  • Applied optimization techniques to design energy-efficient embodied SNNs.
  • Evaluated the framework using event-based car recognition use cases.

Main Results:

  • SNN4Agents achieved 84.12% accuracy on the NCARS dataset.
  • Demonstrated 68.75% memory saving, 3.58x speed-up, and 4.03x energy efficiency improvement.
  • Showcased effective trade-offs between accuracy, latency, memory, and energy consumption.

Conclusions:

  • SNN4Agents provides a systematic approach to optimize embodied SNNs for autonomous agents.
  • The framework enables significant improvements in energy efficiency and performance.
  • Paves the way for widespread adoption of energy-efficient SNNs in autonomous systems.