报告题目Title: Neuro-Inspired Computing with Synaptic and Neuronal Devices
报告人：余诗孟教授，美国乔治亚理工电气工程与计算机工程系
报告时间地点：2019-03-26 （周二下午），4PM-5PM, 中科大北区会议中心（融合楼2楼）
【Abstract】
Neuro-inspired computing is a new computing paradigm that emulates the neural network for information processing. To enable the large-scale neuromorphic system, it is important to develop the compact nanoscale devices to support the synaptic and neuronal functions. In this talk, I will discuss the recent progresses in this domain that integrate the oxide based synaptic and neuronal devices in the neuromorphic hardware such as machine/deep learning accelerators. First, I will discuss the desired characteristics of the HfO2 based resistive synaptic devices (e.g. analog multilevel states, weight tuning linearity, variation/noises) and the NbO2 based oscillation neuron devices, and show the principles of offline training and online training. Next, I will introduce the crossbar array architecture to efficiently implement the weighted sum and weight update operations that are commonly used in the machine/deep learning algorithms, and show array-level experimental demonstrations for these key operations. Lastly, I will show our recent works on doped HfO2 based ferroelectric transistor based synaptic cell design that overcomes the challenges to achieve the high training accuracy for online training.
Bio: Shimeng Yu is an associate professor of electrical and computer engineering at the Georgia Institute of Technology in Atlanta, Georgia. He received the B.S. degree in microelectronics from Peking University, Beijing, China in 2009, and the M.S. degree and Ph.D. degree in electrical engineering from Stanford University, Stanford, California, in 2011 and in 2013, respectively. From 2013 to 2018, he was an assistant professor of electrical and computer engineering at Arizona State University, Tempe, Arizona.
Prof. Yu’s research interests are nanoelectronic devices and circuits for energy-efficient computing systems. His expertise is on the emerging non-volatile memories (e.g., RRAM, ferroelectrics) for different applications, such as machine/deep learning accelerator, neuromorphic computing, monolithic 3D integration, and hardware security, etc.
Among Prof. Yu’s honors, he was a recipient of the NSF Faculty Early CAREER Award in 2016, the ASU Fulton Outstanding Assistant Professor in 2017, the IEEE Electron Devices Society (EDS) Early Career Award in 2017, and the ACM Special Interests Group on Design Automation (SIGDA) Outstanding New Faculty Award in 2018, etc. He is a senior member of the IEEE.
报告人简介：
余诗孟教授，于2009年获得北京大学微电子学学士学位，于 2011年和2013年获得美国斯坦福大学电气工程硕士和博士学位。他目前是美国乔治亚理工电气工程与计算机工程系的AssociateProfessor教授。
余教授的研究兴趣是新兴的纳米器件和电路设计，研究范围包括机器/深度学习，神经形态计算，三维芯片集成，硬件安全，器件和电路抗辐射性能等。课题组在这些领域发表了超过60篇期刊论文和100篇会议论文，包括20余篇IEEE国际电子器件会议（IEDM）以及最近接受的IEEE固态电路会议（ISSCC）。余教授论文被引用次数> 5000，H-index =32。
在余教授的所获得的荣誉中，包括2009年至2012年的斯坦福大学最高研究生奖学金，2010年IEEE电子器件协会（EDS）硕士学生奖学金，2012年IEEE电子器件学会（EDS）博士生奖学金，2015年美国国防部DOD-DTRA青年研究员奖， 2016年获得美国自然科学基金早期职业发展奖（NSF Faculty Early CAREER Award），2017年获得亚利桑那州立大学ASU Fulton杰出助理教授。他现在IEEE国际电路与系统会议（ISCAS），ACM / IEEE电子设计自动化会议（DAC）和IEEE国际电子器件会议（IEDM）等国际会议担任审稿委员会委员。余教授课题组目前承担了来自政府和工业界数百万美元的若干课题，跟半导体公司诸如高通，三星等有着紧密合作。目前课题组成员包括一位博士后和五位博士生，博士招生范围涵盖了半导体器件和材料，数字电路和系统设计，以及机器和深度学习算法优化。