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

## TL;DR

This paper introduces an AutoML-inspired method for efficiently exploring and optimizing neuromorphic architectures using asynchronous parallel search, demonstrated on real-time on-chip learning applications.

## Contribution

It presents a novel asynchronous, parallel model-based optimization framework integrated with simulation for neuromorphic architecture design.

## Key findings

- Effective exploration of neuromorphic configuration space
- Identification of high-performance architectures for specific applications
- Demonstrated viability of application-driven neuromorphic co-design

## 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.