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

arXiv: https://arxiv.org/abs/2302.13210 · HTML: https://mlsanity.com/paper/2302.13210/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 26 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [Ray: A Distributed Framework for Emerging AI Applications](https://mlsanity.com/paper/1712.05889/) (2017)
- [A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators](https://mlsanity.com/paper/2105.12842/) (2021)
- [Whetstone: A Method for Training Deep Artificial Neural Networks for Binary Communication](https://mlsanity.com/paper/1810.11521/) (2018)
- [Coarse scale representation of spiking neural networks: backpropagation through spikes and application to neuromorphic hardware](https://mlsanity.com/paper/2007.06176/) (2020)

## Abstract (arXiv)

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.