A Scalable Approach to Modeling on Accelerated Neuromorphic Hardware

TL;DR

This paper details the software architecture and features of the BrainScaleS-2 neuromorphic hardware system, enabling scalable, efficient, and user-friendly computational research with novel capabilities.

Contribution

It introduces the BrainScaleS-2 Operating System, including new features like multi-compartmental neurons and fast re-configuration for hardware-in-the-loop training.

Findings

Implementation of a comprehensive OS for BrainScaleS-2

Support for novel neuron models and re-configuration

Enhanced usability and scalability features

Abstract

Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the BrainScaleS-2 system, a hybrid accelerated neuromorphic hardware architecture based on physical modeling. We introduce key aspects of the BrainScaleS-2 Operating System: experiment workflow, API layering, software design, and platform operation. We present use cases to discuss and derive requirements for the software and showcase the implementation. The focus lies on novel system and software features such as multi-compartmental neurons, fast re-configuration for hardware-in-the-loop training, applications for the embedded processors, the non-spiking operation mode, interactive platform access, and sustainable hardware/software co-development. Finally, we discuss further developments in terms of hardware scale-up, system usability and efficiency.