AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architectures

• URL: http://arxiv.org/abs/2302.13210v1
• Date: Sun, 26 Feb 2023 02:26:45 GMT
• Title: AutoML for neuromorphic computing and application-driven co-design: asynchronous, massively parallel optimization of spiking architectures
• Authors: Angel Yanguas-Gil and Sandeep Madireddy
• Abstract summary: We have extended AutoML inspired approaches to the exploration and optimization of neuromorphic architectures. We are able to efficiently explore the configuration space of neuromorphic architectures and identify the subset of conditions leading to the highest performance.
• Score: 3.8937756915387505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work we have extended AutoML inspired approaches to the exploration and optimization of neuromorphic architectures. Through the integration of a parallel asynchronous model-based search approach with a simulation framework to simulate spiking architectures, we are able to efficiently explore the configuration space of neuromorphic architectures and identify the subset of conditions leading to the highest performance in a targeted application. We have demonstrated this approach on an exemplar case of real time, on-chip learning application. Our results indicate that we can effectively use optimization approaches to optimize complex architectures, therefore providing a viable pathway towards application-driven codesign.

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