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Lookup NU author(s): Omar AwfORCiD, Professor Rishad ShafikORCiD, Professor Alex Yakovlev, Dr Farhad Merchant
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Data movement costs constitute a significant bottleneck in modern machine learning (ML) systems. When combined with the computational complexity of algorithms, such as neural networks, designing hardware accelerators with low energy footprint remains challenging. Finite state automata (FSA) constitute a type of computation model used as a low-complexity learning unit in ML systems. The implementation of FSA consists of a number of memory states. However, FSA can be in one of the states at a given time. It switches to another state based on the present state and input to the FSA. Due to its natural synergy with memory, it is a promising candidate for in-memory computing for reduced data movement costs. This work focuses on a novel FSA implementation using resistive RAM (ReRAM) for state storage in series with a CMOS transistor for biasing controls. We propose using multi-level ReRAM technology capable of transitioning between states depending on bias pulse amplitude and duration. We use an asynchronous control circuit for writing each ReRAM-transistor cell for the on-demand switching of the FSA. We investigate the impact of the device-to-device and cycle-to-cycle variations on the cell and show that FSA transitions can be seamlessly achieved without degradation of performance. Through extensive experimental evaluation, we demonstrate the implementation of FSA on ITIR ReRAM crossbar.
Author(s): Singh S, Ghazal O, Jha C, Rana V, Drechsler R, Shafik R, Yakovlev A, Patkar S, Merchant F
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 21st IEEE Interregional NEWCAS Conference (NEWCAS 2023)
Year of Conference: 2023
Pages: 1-5
Print publication date: 07/08/2023
Online publication date: 07/08/2023
Acceptance date: 12/04/2023
ISSN: 2474-9672
Publisher: IEEE
URL: https://doi.org/10.1109/NEWCAS57931.2023.10198206
DOI: 10.1109/NEWCAS57931.2023.10198206
Library holdings: Search Newcastle University Library for this item
ISBN: 9798350300246