2022 IGSC - Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware

In this video, we present our paper "Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware" at the 2022 International Green and Sustainable Computing (IGSC) Conference.

The presentation focuses on the efficient deployment of SNNs that result from a DNN-to-SNN conversion method. We present two optimization steps to optimize the accuracy, latency, power, and energy consumption of our SNNs. We then compare the SNN deployed on Intel's Neuromorphic chip, Loihi, to the performance of DNNs on Intel's Neural Compute Stick 2 Edge DL accelerator.

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."

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