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

by Catherine D Schuman, Steven R Young, Bryan P Maldonado Puente, Brian C Kaul
Publication Type
Conference Paper
Book Title
2021 12th International Green and Sustainable Computing Conference (IGSC)
Publication Date
Page Numbers
1 to 8
Conference Name
12th International Green and Sustainable Computing Conference (IGSC)
Conference Location
Pullman, Washington, United States of America
Conference Sponsor
IEEE Computer Society & TC Parallel Processing
Conference Date

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.