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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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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 compact nanoscale devices to support the synaptic and neuronal functions. In this talk, I will discuss recent progress in this domain that integrates oxide based synaptic and neuronal devices in neuromorphic hardware such as machine/deep learning accelerators. First, I will discuss the desired characteristics of HfO2 based resistive synaptic devices (e.g. analog multilevel states, weight tuning linearity, variation/noises) and 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 work on doped HfO2 based ferroelectric transistor based synaptic cell design that overcomes the challenges to achieve high training accuracy for online training.
Bio: Shimeng Yu is an associate professor of electrical and computer engineering at Georgia Tech. He received the B.S. degree in microelectronics from Peking University in 2009, and the M.S. degree and Ph.D. degree in electrical engineering from Stanford University in 2011 and 2013, respectively. From 2013 to 2018, he was an assistant professor of electrical and computer engineering at Arizona State University. 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 DOD-DTRA Young Investigator Award in 2015, 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. He is a senior member of the IEEE.
