RRAM-based hardware implementations of artificial neural networks: progress update and challenges ahead

M. Prezioso; F. Merrikh-Bayat; B. Chakrabarti; D. Strukov

doi:10.1117/12.2235089

27 February 2016 RRAM-based hardware implementations of artificial neural networks: progress update and challenges ahead

M. Prezioso, F. Merrikh-Bayat, B. Chakrabarti, D. Strukov

Author Affiliations +

Proceedings Volume 9749, Oxide-based Materials and Devices VII; 974918 (2016) https://doi.org/10.1117/12.2235089
Event: SPIE OPTO, 2016, San Francisco, California, United States

Abstract

Artificial neural networks have been receiving increasing attention due to their superior performance in many information processing tasks. Typically, scaling up the size of the network results in better performance and richer functionality. However, large neural networks are challenging to implement in software and customized hardware are generally required for their practical implementations. In this work, we will discuss our group’s recent efforts on the development of such custom hardware circuits, based on hybrid CMOS/memristor circuits, in particular of CMOL variety. We will start by reviewing the basics of memristive devices and of CMOL circuits. We will then discuss our recent progress towards demonstration of hybrid circuits, focusing on the experimental and theoretical results for artificial neural networks based on crossbarintegrated metal oxide memristors. We will conclude presentation with the discussion of the remaining challenges and the most pressing research needs.

Citation Download Citation

M. Prezioso, F. Merrikh-Bayat, B. Chakrabarti, and D. Strukov "RRAM-based hardware implementations of artificial neural networks: progress update and challenges ahead", Proc. SPIE 9749, Oxide-based Materials and Devices VII, 974918 (27 February 2016); https://doi.org/10.1117/12.2235089

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