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Memristive Computing for Efficient Inference on Resource Constrained Devices

2022-08-21 07:02:19
Venkatesh Rammamoorthy, Geng Zhao, Bharathi Reddy, Ming-Yang Lin
arXiv_CV
       
Abstract

The advent of deep learning has resulted in a number of applications which have transformed the landscape of the research area in which it has been applied. However, with an increase in popularity, the complexity of classical deep neural networks has increased over the years. As a result, this has leads to considerable problems during deployment on devices with space and time constraints. In this work, we perform a review of the present advancements in non-volatile memory and how the use of resistive RAM memory, particularly memristors, can help to progress the state of research in deep learning. In other words, we wish to present an ideology that advances in the field of memristive technology can greatly influence and impact deep learning inference on edge devices.

Abstract (translated)

URL

https://arxiv.org/abs/2208.10490

PDF

https://arxiv.org/pdf/2208.10490.pdf

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