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

Mittal, Sparsh (2018) A Survey of ReRAM-Based Architectures for Processing-In-Memory and Neural Networks. Machine Learning and Knowledge Extraction, 1 (1). pp. 75-114. ISSN 2504-4990

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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

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IITH Creators:
IITH CreatorsORCiD
Mittal, Sparshhttp://orcid.org/0000-0002-2908-993X
Item Type: Article
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Library Staff
Date Deposited: 24 Sep 2019 11:21
Last Modified: 24 Sep 2019 11:21
URI: http://raiith.iith.ac.in/id/eprint/6377
Publisher URL: http://doi.org/10.3390/make1010005
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