Simulation of Inference Accuracy Using Realistic RRAM Devices

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Simulation of Inference Accuracy Using Realistic RRAM Devices

Mehonic, A; Joksas, D; Ng, W; Buckwell, M; Kenyon, A; (2019) Simulation of Inference Accuracy Using Realistic RRAM Devices. Frontiers in Neuroscience , 13 , Article 593. 10.3389/fnins.2019.00593. Green open access

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Abstract

Resistive Random Access Memory (RRAM) is a promising technology for power efficient hardware in applications of artificial intelligence (AI) and machine learning (ML) implemented in non-von Neumann architectures. However, there is an unanswered question if the device non-idealities preclude the use of RRAM devices in this potentially disruptive technology. Here we investigate the question for the case of inference. Using experimental results from silicon oxide (SiOx) RRAM devices, that we use as proxies for physical weights, we demonstrate that acceptable accuracies in classification of handwritten digits (MNIST data set) can be achieved using non-ideal devices. We find that, for this test, the ratio of the high- and low-resistance device states is a crucial determinant of classification accuracy, with ~96.8% accuracy achievable for ratios >3, compared to ~97.3% accuracy achieved with ideal weights. Further, we investigate the effects of a finite number of discrete resistance states, sub-100% device yield, devices stuck at one of the resistance states, current/voltage non-linearities, programming non-linearities and device-to-device variability. Detailed analysis of the effects of the non-idealities will better inform the need for the optimization of particular device properties.

Type:	Article
Title:	Simulation of Inference Accuracy Using Realistic RRAM Devices
Open access status:	An open access version is available from UCL Discovery
DOI:	10.3389/fnins.2019.00593
Publisher version:	http://dx.doi.org/10.3389/fnins.2019.00593
Language:	English
Additional information:	© 2019 Mehonic, Joksas, Ng, Buckwell and Kenyon. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).
Keywords:	neuromorphic, RRAM, inference, silicon oxide, machine learning
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI:	https://discovery.ucl.ac.uk/id/eprint/10076309