# Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System

## TL;DR

This paper presents a method for training deep spiking neural networks on analog neuromorphic hardware, specifically the BrainScaleS system, by iterative in-the-loop training that compensates for hardware-induced anomalies, achieving high accuracy.

## Contribution

It introduces an in-the-loop training approach that allows deep spiking networks on analog hardware to reach near-software accuracy despite substrate variations.

## Key findings

- Achieved 10,000x acceleration over biological time.
- Networks reach near-ideal accuracy after tens of training iterations.
- Approximate gradient updates suffice for effective training.

## Abstract

Emulating spiking neural networks on analog neuromorphic hardware offers several advantages over simulating them on conventional computers, particularly in terms of speed and energy consumption. However, this usually comes at the cost of reduced control over the dynamics of the emulated networks. In this paper, we demonstrate how iterative training of a hardware-emulated network can compensate for anomalies induced by the analog substrate. We first convert a deep neural network trained in software to a spiking network on the BrainScaleS wafer-scale neuromorphic system, thereby enabling an acceleration factor of 10 000 compared to the biological time domain. This mapping is followed by the in-the-loop training, where in each training step, the network activity is first recorded in hardware and then used to compute the parameter updates in software via backpropagation. An essential finding is that the parameter updates do not have to be precise, but only need to approximately follow the correct gradient, which simplifies the computation of updates. Using this approach, after only several tens of iterations, the spiking network shows an accuracy close to the ideal software-emulated prototype. The presented techniques show that deep spiking networks emulated on analog neuromorphic devices can attain good computational performance despite the inherent variations of the analog substrate.