hxtorch: PyTorch for BrainScaleS-2 -- Perceptrons on Analog Neuromorphic Hardware

TL;DR

This paper introduces hxtorch, a software framework that integrates BrainScaleS-2 neuromorphic hardware with PyTorch, enabling efficient neural network inference and training on analog hardware with automatic network partitioning.

Contribution

It provides the first seamless integration of BrainScaleS-2 hardware into PyTorch, supporting autograd, partitioning, and hardware-in-the-loop training for neuromorphic accelerators.

Findings

Achieved reduced inference and training overheads.

Demonstrated neural network classification on sensor data.

Validated hardware limitations and performance metrics.

Abstract

We present software facilitating the usage of the BrainScaleS-2 analog neuromorphic hardware system as an inference accelerator for artificial neural networks. The accelerator hardware is transparently integrated into the PyTorch machine learning framework using its extension interface. In particular, we provide accelerator support for vector-matrix multiplications and convolutions; corresponding software-based autograd functionality is provided for hardware-in-the-loop training. Automatic partitioning of neural networks onto one or multiple accelerator chips is supported. We analyze implementation runtime overhead during training as well as inference, provide measurements for existing setups and evaluate the results in terms of the accelerator hardware design limitations. As an application of the introduced framework, we present a model that classifies activities of daily living with smartphone sensor data.