A Multi-Threading Kernel for Enabling Neuromorphic Edge Applications

TL;DR

This paper introduces a multi-threading kernel for neuromorphic edge applications, significantly improving processing speed and energy efficiency of Spiking Neural Networks on mobile hardware.

Contribution

It presents a novel multi-threading kernel that enables efficient, load-balanced, and energy-saving neuromorphic processing directly on edge devices.

Findings

4x speed-up on moderately sized SNNs

1.7x speed-up on Synfire network

Up to 70% energy efficiency improvement

Abstract

Spiking Neural Networks (SNNs) have sparse, event driven processing that can leverage neuromorphic applications. In this work, we introduce a multi-threading kernel that enables neuromorphic applications running at the edge, meaning they process sensory input directly and without any up-link to or dependency on a cloud service. The kernel shows speed-up gains over single thread processing by a factor of four on moderately sized SNNs and 1.7X on a Synfire network. Furthermore, it load-balances all cores available on multi-core processors, such as ARM, which run today's mobile devices and is up to 70% more energy efficient compared to statical core assignment. The present work can enable the development of edge applications that have low Size, Weight, and Power (SWaP), and can prototype the integration of neuromorphic chips.