Demonstrating a fully automated software framework for PyTorch-based training on BrainScaleS-2 using a high-speed data communication example

Authors: Elias Arnold, Eric Müller, Philipp Spilger

Presentation type: Poster

Abstract

We present our work towards a PyTorch-integrated general-purpose modeling framework for BrainScaleS-2 (BSS-2). While previous efforts either focused on the rate-based operation mode of BSS-2 or lacked full automation, our approach enables the training of spike-based networks (SNNs) within PyTorch including support for auto differentiation in a fully-automated hardware experiment workflow. Moreover, our framework facilitates seamless transitions between emulating on hardware and simulating in software. To demonstrate the capabilities of our software library, hxtorch.snn, in a real-world application, we investigate the usage of SNNs on our accelerated neuromorphic hardware system BSS-2 for energy-efficient digital signal processing (DSP) in high-speed optical communication systems. We implement an SNN equalizer to compensate for non-linear impairments in a temporal sequence transmitted in an simulated optical short-reach intensity-modulated / direct-detection (IM/DD) link. Our implementation employs Back-Propagation-Through-Time (BPTT) with surrogate gradients for training, and outperforms —in software and on hardware— linear equalization in terms of achieved bit error rate (BER) with only a small hardware penalty.

Are you the author and do you want to add some related links to this page? If so, check out our guide to adding related links and videos.