Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware

TL;DR

This paper explores converting pre-trained deep neural networks into spiking neural networks for deployment on neuromorphic hardware, achieving significant power and energy savings in edge image classification tasks.

Contribution

It provides a comprehensive guide for conversion and introduces techniques to optimize SNN deployment on neuromorphic hardware, improving latency, power, and energy efficiency.

Findings

Loihi consumes up to 27x less power than Intel Neural Compute Stick 2.

Converted SNNs achieve 5x less energy consumption in image classification.

Techniques improve deployment efficiency on neuromorphic hardware.

Abstract

As the technology industry is moving towards implementing tasks such as natural language processing, path planning, image classification, and more on smaller edge computing devices, the demand for more efficient implementations of algorithms and hardware accelerators has become a significant area of research. In recent years, several edge deep learning hardware accelerators have been released that specifically focus on reducing the power and area consumed by deep neural networks (DNNs). On the other hand, spiking neural networks (SNNs) which operate on discrete time-series data, have been shown to achieve substantial power reductions over even the aforementioned edge DNN accelerators when deployed on specialized neuromorphic event-based/asynchronous hardware. While neuromorphic hardware has demonstrated great potential for accelerating deep learning tasks at the edge, the current space of algorithms and hardware is limited and still in rather early development. Thus, many hybrid approaches have been proposed which aim to convert pre-trained DNNs into SNNs. In this work, we provide a general guide to converting pre-trained DNNs into SNNs while also presenting techniques to improve the deployment of converted SNNs on neuromorphic hardware with respect to latency, power, and energy. Our experimental results show that when compared against the Intel Neural Compute Stick 2, Intel's neuromorphic processor, Loihi, consumes up to 27x less power and 5x less energy in the tested image classification tasks by using our SNN improvement techniques.