Real-Time Evolution and Deployment of Neuromorphic Computing at The Edge

• Published in: 2021 12th International Green and Sustainable Computing Conference (IGSC) pp. 1 - 8
• Main Authors: Schuman, Catherine D., Young, Steven R., Maldonado, Bryan P., Kaul, Brian C.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18-10-2021
• Subjects: Adaptation models; combustion control; Computational modeling; engine control unit; FPGA; Green products; internal combustion engine; Neural networks; neuromorphic; Neuromorphic engineering; Real-time systems; Training
• DOI: 10.1109/IGSC54211.2021.9651607

Extremely low power neuromorphic systems are well-suited for deployment to the edge for many applications. In many use cases of neuromorphic computing for control, a spiking neural network is trained off-line using a simulation and then deployed to a neuromorphic system at the edge, where it will operate without ongoing training or learning. However, it may be desirable to continue training or learning at the edge to refine or adapt to the real-world system. In this work, we propose an approach for performing real-time evolutionary optimization for spiking neural networks for neuromorphic deployment at the edge. In particular, we propose a combination of simulation and real-world evaluations, along with feedback from the real-world environment, to train spiking neural networks for continuous deployment to the edge. We show that the real-time evolution at the edge approach achieves comparable performance to an evolution approach that requires constant evaluation in the realworld environment.