Energy and area-efficient mixed-signal-based SRAM computation-in-memory for AI edge devices

The Ph.D. dissertation focuses on mixed-signal-based SRAM computation-in-memory (CIM) for artificial intelligence (AI) edge devices. CIM is an emerging approach that integrates computation capabilities into memory units, reducing data movement and enhancing performance for memory-intensive tasks lik...

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Main Author:	Jo, Yongjun
Other Authors:	Kim Tae Hyoung
Format:	Thesis-Doctor of Philosophy
Language:	English
Published:	Nanyang Technological University 2024
Subjects:	Engineering Compute-in-memory Artificial intelligence In-memory computing
Online Access:	https://hdl.handle.net/10356/181603
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-181603
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spelling	sg-ntu-dr.10356-1816032025-01-02T10:18:25Z Energy and area-efficient mixed-signal-based SRAM computation-in-memory for AI edge devices Jo, Yongjun Kim Tae Hyoung Zheng Yuanjin School of Electrical and Electronic Engineering Centre for Integrated Circuits and Systems THKIM@ntu.edu.sg, YJZHENG@ntu.edu.sg Engineering Compute-in-memory Artificial intelligence In-memory computing The Ph.D. dissertation focuses on mixed-signal-based SRAM computation-in-memory (CIM) for artificial intelligence (AI) edge devices. CIM is an emerging approach that integrates computation capabilities into memory units, reducing data movement and enhancing performance for memory-intensive tasks like deep neural networks (DNNs). CIM mainly deals with multiply-and-accumulate (MAC) operations which consist most of the computing burden in DNN. Additionally, mixed-signal-based CIM has many advantages over digital-based CIM in terms of area, throughput, and power in low-precision DNN (1-4b) as able to accumulate multiplication results at once with analog domain (voltage, current and charge, etc.) Therefore, mixed-signal-based CIM is an attractive option as low-precision DNNs have achieved competitive precision so far. Doctor of Philosophy 2024-12-10T07:59:39Z 2024-12-10T07:59:39Z 2023 Thesis-Doctor of Philosophy Jo, Y. (2023). Energy and area-efficient mixed-signal-based SRAM computation-in-memory for AI edge devices. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181603 https://hdl.handle.net/10356/181603 10.32657/10356/181603 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Engineering Compute-in-memory Artificial intelligence In-memory computing
spellingShingle	Engineering Compute-in-memory Artificial intelligence In-memory computing Jo, Yongjun Energy and area-efficient mixed-signal-based SRAM computation-in-memory for AI edge devices
description	The Ph.D. dissertation focuses on mixed-signal-based SRAM computation-in-memory (CIM) for artificial intelligence (AI) edge devices. CIM is an emerging approach that integrates computation capabilities into memory units, reducing data movement and enhancing performance for memory-intensive tasks like deep neural networks (DNNs). CIM mainly deals with multiply-and-accumulate (MAC) operations which consist most of the computing burden in DNN. Additionally, mixed-signal-based CIM has many advantages over digital-based CIM in terms of area, throughput, and power in low-precision DNN (1-4b) as able to accumulate multiplication results at once with analog domain (voltage, current and charge, etc.) Therefore, mixed-signal-based CIM is an attractive option as low-precision DNNs have achieved competitive precision so far.
author2	Kim Tae Hyoung
author_facet	Kim Tae Hyoung Jo, Yongjun
format	Thesis-Doctor of Philosophy
author	Jo, Yongjun
author_sort	Jo, Yongjun
title	Energy and area-efficient mixed-signal-based SRAM computation-in-memory for AI edge devices
title_short	Energy and area-efficient mixed-signal-based SRAM computation-in-memory for AI edge devices
title_full	Energy and area-efficient mixed-signal-based SRAM computation-in-memory for AI edge devices
title_fullStr	Energy and area-efficient mixed-signal-based SRAM computation-in-memory for AI edge devices
title_full_unstemmed	Energy and area-efficient mixed-signal-based SRAM computation-in-memory for AI edge devices
title_sort	energy and area-efficient mixed-signal-based sram computation-in-memory for ai edge devices
publisher	Nanyang Technological University
publishDate	2024
url	https://hdl.handle.net/10356/181603
_version_	1821237165370114048

Energy and area-efficient mixed-signal-based SRAM computation-in-memory for AI edge devices

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