Memristive Computing for Efficient Inference on Resource Constrained Devices

TL;DR

This paper reviews how memristive technology, especially resistive RAM, can enable efficient deep learning inference on resource-constrained edge devices by leveraging non-volatile memory advancements.

Contribution

It presents a comprehensive review of memristive memory's potential to improve deep learning inference on edge devices, highlighting recent technological progress.

Findings

Memristors can reduce power consumption for inference.

Resistive RAM enables compact neural network implementations.

Advances in memristive technology can accelerate edge AI deployment.

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.