Huaqiang Wu

Tsinghua University, China

Bio: Prof. Huaqiang Wu is currently the Dean of School of Integrated Circuits, Tsinghua University, Beijing, China. He received bachelor degree in MSE from Tsinghua University in 2000. In 2005, he received Ph.D. degree in EE from Cornell University. After that, he worked for AMD and Spansion as Senior Research Engineer, working on advanced Flash memory devices. He joined Tsinghua University in 2009 as associate professor, and became full professor in 2018. His research topics include the emerging memory devices and alternative computing paradigms. He has authored more than 200 journal papers and conference proceedings on Nature, Nature Nanotechnology, Nature Electronics, IEDM, VLSI, etc. He serves as an associate editor of IEEE EDL, the TPC chair of EDTM 2021, and TPC member of VLSI and VLSI-TSA. He has given multiple invited talks on IEDM, IRPS, IMW, MRS, GLS-VLSI, etc. He received the inaugural Xplorer prize and the National Science Fund for Distinguished Young Scholars by NSFC.

Speech Title: Memristor-based Energy-Efficient Neuromorphic Computing

As a type of neuromorphic devices, memristor can work like an synapse by tuning its conductance continuously under external stimuli. A memristor array can naturally process neural network calculation with a computing-in-memory manner, which means the memristor device is used for both weight storage and multiplication and accumulation calculation. This computing-in-memory function can significantly reduce the data movement between memory unit and computing unit, and thus save a lot of energy and time. In this talk, we will present the recent progress of fabrication and design of memristor based neuromorphic chips. We demonstrate that the memristor chip can improve the energy efficiency by orders when comparing with traditional pure CMOS based AI chips.

Elisabetta Chicca

University of Groningen, The Netherlands

Bio: Elisabetta Chicca obtained a "Laurea" degree (M.Sc.) in Physics from the University of Rome 1 "La Sapienza", Italy in 1999 with a thesis on CMOS spike-based learning. In 2006 she received a Ph.D. in Natural Science from the Swiss Federal Institute of Technology Zurich (ETHZ, Physics department) and in Neuroscience from the Neuroscience Center Zurich. E. Chicca has carried out her research as a Postdoctoral fellow (2006-2010) and as a Group Leader (2010-2011) at the Institute of Neuroinformatics (University of Zurich and ETH Zurich) working on development of neuromorphic signal processing and sensory systems.

Between 2011 and 2020 she lead the Neuromorphic Behaving Systems research group at Bielefeld University (Faculty of Technology and Cognitive Interaction Technology Center of Excellence, CITEC). In 2021 she established the Bio-Inspired Circuits and Systems group at the University of Groningen. Her current interests are in the development of CMOS models of cortical circuits for brain-inspired computation, learning in spiking CMOS neural networks and memristive systems, bio-inspired sensing (vision, touch, olfaction, audition, active electrolocation) and motor control. She combines these research approaches with the aim of understanding neural computation by constructing behaving agents which can robustly operate in real-world environments.

Speech Title: Biologically Realistic Learning in Full Custom CMOS Asynchronous Systems

Synaptic plasticity is considered to be the basis of learning and memory in the brain. It goes from low level task-specific learning to high level cognition. Understanding the computational foundations of synaptic plasticity is therefore a growing research that inspires progress in the design of autonomous adaptive systems. In that perspective, a large number of brain-inspired learning rules have been modeled and implemented. Locality, a fundamental computational principle of biological synaptic plasticity, is a key requirement for physical implementation of learning rules. In this talk we provide an overview of models and asynchronous circuits for spike-based local synaptic plasticity. This overview provides the background for presenting our recent work aimed at the implementation of learning systems based on CMOS and BEOL compatible memristive devices.

Gang Pan

Zhejiang University, China

Bio: Gang Pan is a Distinguished Professor of Zhejiang University, the Executive Deputy Director of the State Key Laboratory of Brain-Machine Intelligence. He received the B.Eng. and Ph.D. degrees from Zhejiang University in 1998 and 2004 respectively. His interests include artificial intelligence, brain-inspired computing, brain-machine interfaces, and pervasive computing. He has co-authored more than 100 refereed papers, and has more than 50 patents granted. Dr. Pan is a recipient of NSFC for Distinguished Young Scholars, IEEE TCSC Award for Excellence (Middle Career Researcher), CCF-IEEE CS Young Scientist Award, TOP-10 Achievements in Science and Technology in Chinese Universitie, National Science and Technology Progress Award, and several best paper awards. He serves as an associate editor of IEEE Trans. Neural Networks and Learning Systems, IEEE Trans. Cognitive and Developmental Systems, Coginative Neurodynamics, etc.

Speech Title: Neuromorphic Computer: Progresses, Challenges, and Practices

The field of neuromorphic computing has gained significant attention in recent years due to its potential to revolutionize traditional computing paradigms. This talk will introduce the concept of neuromorphic computer, and provide an overview of the progresses, challenges, and practices in neuromorphic computer development. This talk will introduce the fundamental principles of neuromorphic computing, which draws inspiration from the intricate workings of the human brain to design efficient and intelligent computing systems. It will delve into the progress made in developing neuromorphic hardware, such as specialized chips and architectures that mimic neural networks' parallel and distributed nature. It will also explore the challenges faced in the field. Finally, our practices in building a neuromorphic computer will be introduces.