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Mach. Learn. Knowl. Extr. 2018, 1(1), 75-114; https://doi.org/10.3390/make1010005

A Survey of ReRAM-Based Architectures for Processing-In-Memory and Neural Networks

Department of Computer Science and Engineering, IIT Hyderabad, Telangana 502285, India
Received: 15 March 2018 / Revised: 16 April 2018 / Accepted: 26 April 2018 / Published: 30 April 2018

Abstract

As data movement operations and power-budget become key bottlenecks in the design of computing systems, the interest in unconventional approaches such as processing-in-memory (PIM), machine learning (ML), and especially neural network (NN)-based accelerators has grown significantly. Resistive random access memory (ReRAM) is a promising technology for efficiently architecting PIM- and NN-based accelerators due to its capabilities to work as both: High-density/low-energy storage and in-memory computation/search engine. In this paper, we present a survey of techniques for designing ReRAM-based PIM and NN architectures. By classifying the techniques based on key parameters, we underscore their similarities and differences. This paper will be valuable for computer architects, chip designers and researchers in the area of machine learning. View Full-Text
Keywords: review; memristor; resistive memory; artificial intelligence; machine learning; deep learning; hardware architecture; processing-in-memory; non-volatile memory; emerging memory technology review; memristor; resistive memory; artificial intelligence; machine learning; deep learning; hardware architecture; processing-in-memory; non-volatile memory; emerging memory technology
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Mittal, S. A Survey of ReRAM-Based Architectures for Processing-In-Memory and Neural Networks. Mach. Learn. Knowl. Extr. 2018, 1, 75-114.

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