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ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Neuromorphic Engineering
Volume 18 - 2024 |
doi: 10.3389/fnins.2024.1431222
This article is part of the Research Topic Brain-Inspired Computing: From Neuroscience to Neuromorphic Electronics for new forms of Artificial Intelligence View all 6 articles
Energy-Aware Bio-Inspired Spiking Reinforcement Learning System Architecture for Real-Time Autonomous Edge Applications
Provisionally accepted- 1 Oslo Metropolitan University, Oslo, Norway
- 2 Cornell University, Ithaca, New York, United States
Mobile, low-cost, and energy-aware operation of Artificial Intelligence (AI) computations in smart circuits and autonomous robots will play an important role in the next industrial leap in intelligent automation and assistive devices. Neuromorphic hardware with spiking neural network (SNN) architecture utilizes insights from biological phenomena to offer encouraging solutions.Previous studies have proposed reinforcement learning (RL) models for SNN responses in the rat hippocampus to an environment where rewards depend on the context. The scale of these models matches the scope and capacity of small embedded systems in the framework of Internet-of-Bodies (IoB), autonomous sensor nodes, and other edge applications. Addressing energy-efficient artificial learning problems in such systems enables smart micro-systems with edge intelligence. A novel bio-inspired RL system architecture is presented in this work, leading to significant energy consumption benefits without foregoing real-time autonomous processing and accuracy requirements of the context-dependent task. The hardware architecture successfully models features analogous to synaptic tagging, changes in the exploration schemes, synapse saturation, and spatially localized task-based activation observed in the brain. The design has been synthesized, simulated, and tested on Intel MAX10 Field-Programmable Gate Array (FPGA). The problem-based bio-inspired approach to SNN edge architectural design results in 25X reduction in average power compared to the state-of-the-art for a test with real-time context learning and 30 trials. Furthermore, 940x lower energy consumption is achieved due to improvement in the execution time.
Keywords: reinforcement learning, System architecture, Spiking Neural network, neuromorphic hardware, low-cost, Low-energy, context-dependent task, Autonomous
Received: 11 May 2024; Accepted: 04 Sep 2024.
Copyright: © 2024 Okonkwo, Abdelfattah, Mirtaheri and Muhtaroglu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Ali Muhtaroglu, Oslo Metropolitan University, Oslo, Norway
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