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Energy-aware bio-inspired spiking reinforcement learning system architecture for real-time autonomous edge
Joshua Ifeanyi Okonkwo1, Mohamed S Abdelfattah2, Peyman Mirtaheri3,4
1Biomedical Engineering MS Program, Oslo Metropolitan University, Oslo, Norway.
Frontiers in Neuroscience
|October 8, 2024
Summary
This study introduces a novel bio-inspired reinforcement learning (RL) system for spiking neural networks (SNNs) that significantly cuts energy use in edge AI. The new architecture achieves substantial power and energy savings for autonomous systems.
Area of Science:
- Neuromorphic engineering
- Artificial Intelligence (AI)
- Robotics
Background:
- Energy-efficient AI is crucial for intelligent automation, smart circuits, and autonomous robots.
- Spiking neural networks (SNNs) offer bio-inspired solutions for low-power AI.
- Existing reinforcement learning (RL) models for SNNs are being scaled for edge applications.
Purpose of the Study:
- To present a novel bio-inspired RL system architecture for SNNs.
- To achieve significant energy savings in edge AI applications.
- To maintain real-time autonomous processing and accuracy.
Main Methods:
- Developed a bio-inspired RL system architecture mimicking brain functions like synaptic tagging and localized activation.
- Modeled features such as exploration schemes and synapse saturation.
- Synthesized, simulated, and tested the design on an Intel MAX10 Field-Programmable Gate Array (FPGA).
Main Results:
- Achieved a 25X reduction in average power compared to state-of-the-art for real-time context learning.
- Attained 940x lower energy consumption due to improved execution time.
- Demonstrated successful modeling of brain-analogous features in hardware.
Conclusions:
- The bio-inspired SNN edge architecture offers substantial energy efficiency benefits.
- The system meets real-time processing and accuracy requirements for context-dependent tasks.
- This approach enables smarter micro-systems with enhanced edge intelligence.
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