AI-Enhanced Circuit Design and Advanced Memory Computing

by Yongfu Li, Yueting Li, Fakhrul Zaman Rokhani, Amara Amara

Overview

Welcome to this collection of presentations from our comprehensive volume on AI-Enhanced Circuit Design and Advanced Memory Computing. This book presents cutting-edge research and developments from leading experts shaping the future of integrated circuit architectures and computing paradigms.

Section I covers foundational principles of AI-driven circuit design, featuring how AI empowers the design and optimization of analog-to-digital converters. Section II delves into Near-Memory Computing (NMC), with an in-depth exploration of NMC architectures and their transformative potential for computing efficiency. Section III focuses on Processing-In-Memory paradigms, where ReRAM-based accelerators are tailored for scientific computing workloads, alongside a comprehensive overview of in-memory hyperdimensional computing algorithms, circuit implementations, and applications.

This collection offers a focused yet broad perspective on emerging AI-enhanced design methodologies and memory-centric computing architectures, serving as a valuable resource for researchers, engineers, and technologists advancing next-generation computing systems.

Product Details

ISBN-13:	9788743808619
Publisher:	River Publishers
Publication date:	12/23/2025
Sold by:	Barnes & Noble
Format:	eBook
Pages:	153
File size:	73 MB
Note:	This product may take a few minutes to download.

About the Author

Yongfu Li received the B. Eng. and Ph.D. degrees from the Department of Electrical and Computing Engineering, National University of Singapore, Singapore. He is currently an Associate Professor in the Department of Micro and Nano Electronics Engineering and the MoE Key Laboratory of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China. From 2013 to 2014, he served as a Research Engineer at NUS. He was a Senior Engineer from 2014 to 2016, a Principal Engineer from 2016 to 2018, and a Member of Technical Staff from 2018 to 2019 at GLOBALFOUNDRIES, where he served as a Design-to-Manufacturing (DFM) Computer-Aided Design (CAD) research and development engineer. His research interests include analog/mixed signal circuits, data converters, power converters, biomedical signal processing with deep learning techniques, and DFM circuit automation

YUETING LI received the Ph.D. degree in the School of Integrated Circuit Science and Engineering, Beihang University. She is currently an associate Professor at Beihang University. Her research interests primarily include system integration, the application of MRAM, near-memory computing, and the design of neural network accelerators. She won the University Demo Best Demonstration in ACM/SIGDAUD’21, Best Presentation Award in ICCC’21, and a Finalist in the ISLPED’21 Design Contest

Fakhrul Zaman Rokhani received the Ph.D. degree from the University of Minnesota, USA. He was a visiting professor at the Intel Penang Design Center and Huawei Technologies, a visiting scholar at the ASIC and Systems State Key Laboratory, Fudan University, and a visiting professor at several other universities. He is currently an Associate Professor with Universiti Putra Malaysia and heads the Smart System and System-on-Chip (S3oC) Research Group. His current research interests include low-power, energy-efficient system-on-chip (SoC) design and automation, IoT system integration, and sensor technologies for food quality applications. He serves as the Vice-President for Education and Communications for the CAS Society from 2023 to 2024. He is a member of the IEEE Future Directions Steering Committee for Global Semiconductors. On the publication front, he was a Guest Editor of IEEE Transactions on Circuits and Systems—I: Regular Papers (TCAS-I) and IEEE Transactions on Circuits and Systems—II: Express Briefs, and Technical Program/Publication Chair/Track Chairs/Embedded Workshop Chair of several IEEE CASS conferences. He is an Associate Editor of TCAS-I and the CAS Society Newsletter.

Amara Amara is currently a Professor with Hangzhou International Innovation Institute, Beihang University, and formerly a Professor with Paris Higher Institute of Electronics (ISEP). He has been engaged in research on low-power, advanced non-volatile memory design and low-power digital circuit design. He currently serves as the Chair of the IEEE CASS Nomination Committee and a member of the Steering Committee, and is the General Chair of IEEE ISCAS’24. Previously, he served as the Co-Chair of IEEE ISICAS'21, '19, and '18, and as the General Chair of IEEE ISICAS’21. From 2020 to 2021, he served as Chair of IEEE CASS, and from 2014 to 2016, he chaired the IEEE France Section. He was formerly the Deputy Managing Director of the Paris Higher Institute of Electronics. Also, he served as an Expert for research institutions, such as the Swiss National Science Foundation and French National Research Agency. He has jointly supervised 19 doctoral students and published nearly 200 articles in SCI journals. Collaborating with institutions such as the French Atomic Energy and Alternative Energies Commission (CEA-LETI), he has contributed to the publication of works including “Double-Gate FD SOI Devices and Circuits,” “Emerging Technologies and Circuits,” and “TFET Integrated Circuits: From Theory to Reality.”

Table of Contents

Section I: Foundations of AI-Enhanced Circuit Design • Chapter 1: AI-Empowered Design of Analog-to-Digital Converters, José M. de la Rosa (Introduction to AI's role in circuit design and optimization.) Section II: Near-Memory Computing Architectures • Chapter 2: Advanced Architecture Research on Near Memory Computing (NMC), Guanyu Sun (Detailed exploration of NMC concepts and its architecture.) Section III: Processing-In-Memory Paradigms • Chapter 3: ReRAM-Based Processing-in-Memory Accelerator for Scientific Computing, Jin Zhou (Application-focused discussion on ReRAM in processing-in-memory.) • Chapter 4: In-Memory Hyperdimensional Computing: Algorithms, Circuits, and Applications, Weiqiang Liu (Overview of hyperdimensional computing and in-memory applications.)