# Gradient-descent hardware-aware training and deployment for mixed-signal Neuromorphic processors

arXiv: https://arxiv.org/abs/2303.12167 · HTML: https://mlsanity.com/paper/2303.12167/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Training Deep Spiking Neural Networks using Backpropagation](https://mlsanity.com/paper/1608.08782/) (2016)
- [SuperSpike: Supervised learning in multi-layer spiking neural networks](https://mlsanity.com/paper/1705.11146/) (2017)
- [A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)](https://mlsanity.com/paper/1708.04198/) (2017)
- [Neuromorphic electronic circuits for building autonomous cognitive systems](https://mlsanity.com/paper/1403.6428/) (2014)
- [Real-time ultra-low power ECG anomaly detection using an event-driven neuromorphic processor](https://mlsanity.com/paper/1911.05521/) (2019)
- [Mapping Spiking Neural Networks to Neuromorphic Hardware](https://mlsanity.com/paper/1909.01843/) (2019)
- [Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors](https://mlsanity.com/paper/2102.06408/) (2021)

## Built on by (most cited first, 4 of 4)

- [Distributed Representations Enable Robust Multi-Timescale Symbolic Computation in Neuromorphic Hardware](https://mlsanity.com/paper/2405.01305/) (2024)
- [Noise Adaptor in Spiking Neural Networks](https://mlsanity.com/paper/2312.05290/) (2023)
- [A Realistic Simulation Framework for Analog/Digital Neuromorphic Architectures](https://mlsanity.com/paper/2409.14918/) (2024)
- [Practical Bayesian Inference for Speech SNNs: Uncertainty and Loss-Landscape Smoothing](https://mlsanity.com/paper/2604.08624/) (2026)

## Abstract (arXiv)

Mixed-signal neuromorphic processors provide extremely low-power operation for edge inference workloads, taking advantage of sparse asynchronous computation within Spiking Neural Networks (SNNs). However, deploying robust applications to these devices is complicated by limited controllability over analog hardware parameters, as well as unintended parameter and dynamical variations of analog circuits due to fabrication non-idealities. Here we demonstrate a novel methodology for ofDine training and deployment of spiking neural networks (SNNs) to the mixed-signal neuromorphic processor DYNAP-SE2. The methodology utilizes gradient-based training using a differentiable simulation of the mixed-signal device, coupled with an unsupervised weight quantization method to optimize the network's parameters. Parameter noise injection during training provides robustness to the effects of quantization and device mismatch, making the method a promising candidate for real-world applications under hardware constraints and non-idealities. This work extends Rockpool, an open-source deep-learning library for SNNs, with support for accurate simulation of mixed-signal SNN dynamics. Our approach simplifies the development and deployment process for the neuromorphic community, making mixed-signal neuromorphic processors more accessible to researchers and developers.