Demonstrating the Advantages of Analog Wafer-Scale Neuromorphic Hardware

TL;DR

This paper showcases the advantages of analog wafer-scale neuromorphic hardware, specifically the BrainScaleS-1 system, in accelerating complex neural network simulations with reduced energy consumption, compared to traditional methods.

Contribution

The paper demonstrates the capabilities of the BrainScaleS-1 neuromorphic system and its integration with conventional simulations for complex neural network modeling.

Findings

Emulation time is significantly reduced on BrainScaleS-1.

Energy consumption is lower compared to traditional simulations.

Effective modeling of complex neural networks like cortical microcircuits.

Abstract

As numerical simulations grow in size and complexity, they become increasingly resource-intensive in terms of time and energy. While specialized hardware accelerators often provide order-of-magnitude gains and are state of the art in other scientific fields, their availability and applicability in computational neuroscience is still limited. In this field, neuromorphic accelerators, particularly mixed-signal architectures like the BrainScaleS systems, offer the most significant performance benefits. These systems maintain a constant, accelerated emulation speed independent of network model and size. This is especially beneficial when traditional simulators reach their limits, such as when modeling complex neuron dynamics, incorporating plasticity mechanisms, or running long or repetitive experiments. However, the analog nature of these systems introduces new challenges. In this paper we demonstrate the capabilities and advantages of the BrainScaleS-1 system and how it can be used in combination with conventional software simulations. We report the emulation time and energy consumption for two biologically inspired networks adapted to the neuromorphic hardware substrate: a balanced random network based on Brunel and the cortical microcircuit from Potjans and Diesmann.