Abstract
The increasing demand for processing large volumes of data for machine learning (ML) 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 article, we present In-Memory comPuting architecture based on Y-FlAsh technology for Coalesced Tsetlin machine inference (IMPACT), underpinned on a cutting-edge memory device, Y-Flash, fabricated on a 180 nm complementary metal oxide semiconductor (CMOS) process. Y-Flash devices have recently been demonstrated for digital and analogue memory applications; they offer high yield, non-volatility and low power consumption. IMPACT leverages the Y-Flash array to implement the inference of a novel ML algorithm: coalesced Tsetlin machine (CoTM) based on propositional logic. CoTM utilizes Tsetlin automata (TA) to create Boolean feature selections stochastically across parallel clauses. IMPACT is organized into two computational crossbars for storing the TA and weights. Through validation on the MNIST dataset, IMPACT achieved [Formula: see text] accuracy. IMPACT demonstrated improvements in energy efficiency, e.g. factors of 2.23 over CNN-based ReRAM, 2.46 over neuromorphic using NOR-Flash and 2.06 over DNN-based phase-change memory (PCM), suited for modern ML inference applications.This article is part of the theme issue 'Emerging technologies for future secure computing platforms'.