本期“至善芯语”&“芯”太湖集成电路系列讲座邀请到了新加坡南洋理工大学Tony Tae-Hyoung Kim教授为我们做学术报告，欢迎各位行业同仁参与、交流和学习。
讲座信息：
报告人：新加坡南洋理工大学Tony Tae-Hyoung Kim
主题：Design of computing-in-memory and its application in tiny machine learning accelerators
时间： 2024年7月19日（周五）9:30-10:30
地点： EDA国创中心501会议室
嘉宾介绍：
Tony Tae-Hyoung Kim
新加坡南洋理工大学
Tony Tae-Hyoung Kim (Senior Member, IEEE) received the B.S. and M.S. degrees in electrical engineering from Korea University, Seoul, South Korea, in 1999 and 2001, respectively, and the Ph.D. degree in electrical and computer engineering from the University of Minnesota, Minneapolis, MN, USA, in 2009. From 2001 to 2005, he was with Samsung Electronics, Hwasung, South Korea. In 2009, he joined Nanyang Technological University, Singapore, where he is currently an Associate Professor.
He has published over 200 papers in journals and conferences and holds 20 U.S. and Korean patents registered. His current research interests include computing-in-memory for machine learning, ultra-low power circuits and systems for smart edge computing, low-power and high-performance digital, mixed-mode, and memory circuit design, variation-tolerant circuits and systems, and emerging memory circuits for neural networks.
Dr. Kim received Distinguished Design Award at A-SSCC2023 Student Design Contest, Best Paper Award (Gold Prize) in IEEE/IEIE ICCE-Asia2021, Korean Federation of Science and Technology (KOFST) Award in 2021, Best Demo Award at APCCAS2016, Low Power Design Contest Award at ISLPED2016, Best Paper Awards at 2014 and 2011 ISOCC, AMD/CICC Student Scholarship Award at IEEE CICC2008, DAC/ISSCC Student Design Contest Award in 2008, Samsung Humantech Thesis Award in 2008, 2001, and 1999, and ETRI Journal Paper of the Year Award in 2005. He was the Chair of the IEEE Solid-State Circuits Society Singapore Chapter in 2015-2016 and is Chair-Elect/Secretary of the IEEE Circuits and Systems Society VSATC. He has served on numerous IEEE conferences as a Committee Member. He serves as a Corresponding Guest Editor for the IEEE JOURNAL on EMERGING and SELECTED TOPICS in CIRCUITS and SYSTEMS (JETCAS), a Guest Editor for the IEEE TRANSACTIONS on BIOMEDICAL CIRCUITS and SYSTEMS (TBioCAS), an Associate Editor for the IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS and IEEE ACCESS.
报告摘要：The recent development in neural networks has required massive data transfer between memory and processing elements for data processing. This heavy data transfer leads to substantial energy overhead and limits the overall performance of the neural networks. Computing-in-memory (CIM) has attracted the research community’s attention because of the significant improvement in energy efficiency by minimizing the energy-hungry data transfer. CIM designs can employ either analog computing or digital computing, while each has its pros and cons. In this talk, I will present the basics of CIM design and various challenges. After that, various state-of-the-art CIM macros will be introduced. I will also discuss the pros and cons of analog and digital CIM macros. Finally, a tiny machine learning accelerator utilizing CIM will be briefly introduced.