Impact of Operating Temperature on Pattern Recognition Accuracy of Resistive Array-Based Hardware Neural Networks
SCIE
SCOPUS
- Title
- Impact of Operating Temperature on Pattern Recognition Accuracy of Resistive Array-Based Hardware Neural Networks
- Authors
- Choi, Wooseok; Lee, Chuljun; Noh, Sujung; Lee, Jisung; Lee, Hansaem; Kim, Seyoung; Hwang, Hyunsang
- Date Issued
- 2021-05
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Abstract
- In hardware neural networks (HNNs), different operating temperatures cause variation in conductance of resistive arrays, and they can significantly distort the information of the synaptic weights, leading to a considerable loss in pattern recognition accuracy. In this study, a WOx-based resistive device is characterized with varying ambient temperatures, and 1k-bit synapse arrays are evaluated. A systematic analysis of the impact of operating temperature on the array-based HNNs is executed using neural network simulations. Moreover, we propose a temperature compensator (TC) that can mitigate anomalous array behavior without modifying the readout circuitry. Our results have demonstrated successful accuracy recovery of the array-based HNN over a wide range of operating temperatures.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/106845
- DOI
- 10.1109/LED.2021.3065367
- ISSN
- 0741-3106
- Article Type
- Article
- Citation
- IEEE ELECTRON DEVICE LETTERS, vol. 42, no. 5, page. 763 - 766, 2021-05
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