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Lookup NU author(s): Omar AwfORCiD, Gang Mao, Tian Lan, Jesse Ojukwu, Dr Fei Xia, Professor Alex YakovlevORCiD, Professor Rishad ShafikORCiD
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Tsetlin machines (TMs) are a novel machine learning paradigm based on learning automata and Boolean logic inference, with better energy-efficiency and explainability than neural networks. This work exploits non-volatile ReRAM-transistor memory arrays to perform efficient in-memory TM computing. To accommodate the large timing variability of ReRAM devices and enhance energy efficiency and speed, the control path is implemented with quasi delay-insensitive (QDI) asynchronous circuits. The design of these circuits are derived and synthesized from their signal-transition graph specifications using the Workcraft tool. The resulting circuits offer high event-driven controllability and high variation tolerance for the mixed-signal ReRAM data path. Compared to state of the art TM hardware, the new TM design uses less than 5% of the power to achieve better than4× the performance.
Author(s): Ghazal O, Mao G, Lan T, Ojukwu J, Xia F, Yakovlev A, Shafik R
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: IEEE International Symposium on Circuits and Systems (ISCAS 2023)
Year of Conference: 2023
Pages: 1-5
Print publication date: 21/07/2023
Online publication date: 21/07/2023
Acceptance date: 20/01/2023
ISSN: 2158-1525
Publisher: IEEE
URL: https://doi.org/10.1109/ISCAS46773.2023.10181560
DOI: 10.1109/ISCAS46773.2023.10181560
Library holdings: Search Newcastle University Library for this item
ISBN: 9781665451093