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Lookup NU author(s): Omar AwfORCiD, Dr Farhad Merchant, Professor Alex Yakovlev, Professor Rishad ShafikORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The increasing demand for processing large volumes of data for machine learning models has pushed data bandwidth requirements beyond the capability of traditional von Neumann architecture. In-memory computing (IMC) has recently emerged as a promising solution to address this gap by enabling distributed data storage and processing at the micro-architectural level, significantly reducing both latency and energy. In this paper, we present the IMPACT( In-Memory comPuting architecture based on Y-FlAsh technology for Coalesced Tsetlin machine inference), underpinned on a cutting-edge memory device, Y-Flash, fabricated on a 180 nm CMOS process. Y-Flash devices have recently been demonstrated for digital and analog memory applications, offering high yield, non-volatility, and low power consumption. The IMPACT leverages the Y-Flash array to implement the inference of a novel machine learning algorithm: coalesced Tsetlin machine (CoTM) based on propositional logic. CoTM utilizes Tsetlin automata (TA) to create Boolean feature selections stochastically across parallel clauses. The IMPACT is organized into two computational crossbars for storing the TA and weights. Through validation on the MNIST dataset, IMPACT achieved 96.3% accuracy. The IMPACT demonstrated improvements in energy efficiency, e.g., 2.23X over CNN-based ReRAM, 2.46X over Neuromorphic using NOR-Flash, and 2.06X over DNN-based PCM, suited for modern ML inference applications.
Author(s): Ghazal O, Wang W, Kvatinsky S, Merchant F, Yakovlev A, Shafik R
Publication type: Article
Publication status: Published
Journal: Royal Society of London. Philosophical Transactions A. Mathematical, Physical and Engineering Sciences
Year: 2025
Volume: 383
Online publication date: 16/01/2025
Acceptance date: 25/11/2024
Date deposited: 04/12/2024
ISSN (print): 1364-503X
ISSN (electronic): 1471-2962
Publisher: The Royal Society Publishing
URL: https://doi.org/10.1098/rsta.2023.0393
DOI: 10.1098/rsta.2023.0393
Data Access Statement: This article has no additional data.
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