Shimeng Yu has been named as a Distinguished Lecturer for the IEEE Circuits and Systems Society (CASS) for 2021-2022. Yu is an associate professor in the Georgia Tech School of Electrical and Computer Engineering (ECE).

Yu leads the Laboratory for Emerging Devices and Circuits, where he and his team design energy-efficient computing systems based on emerging nanoelectronic devices. An example of his group’s work is the development of hardware accelerators for machine/deep learning with CMOS and beyond CMOS technologies.

The two areas in which Yu will present lectures include:

• Circuit Design and Silicon Prototypes for Compute-in-Memory for Deep Learning Inference Engine
• NeuroSim: A Benchmark Framework of Compute-in-Memory Hardware Accelerators from Devices/Circuits to Architectures/Algorithms

Yu has been a member of the ECE faculty since 2018. Prior to joining Georgia Tech, he was on the ECE faculty at Arizona State University for five years. Yu has received numerous awards over the last several years, including the ACM/IEEE Design Automation Conference 40 Under-40 Innovators Award, Semiconductor Research Corporation Young Faculty Award, ACM Special Interest Group on Design Automation Outstanding New Faculty Award, and IEEE Electron Devices Society Early Career Award. 

Yu has been active in IEEE CASS activities, including serving on the technical committee of Nanoelectronics and Gigascale Systems and the review committee of the IEEE International Symposium on Circuits and Systems (ISCAS). He currently serves as the associate editor of the IEEE Journal on Exploratory Solid-State Computational Devices and Circuits (JxCDC). More information on IEEE CASS can be found at https://ieee-cas.org/

Tushar Krishna has been chosen as one of the recipients of Facebook Research's Faculty Award for AI System Hardware/Software Co-Design. Krishna was among the eight winners who were selected from 88 worldwide submissions.  

The title of Krishna’s award-winning project is “ML-Driven HW-SW Co-Design of Efficient Tensor Core Architectures.” In this project, co-design implies simultaneous design and optimization of several aspects of the system, including hardware and software, to achieve a set target for a given system metric, such as throughput, latency, power, size, or any combination these factors. 

Artificial intelligence (AI) and deep learning have been particularly amenable to such co-design processes across various parts of the software and hardware stack, leading to a variety of novel algorithms, numerical optimizations, and AI hardware. Krishna’s proposal focuses on creating an automated machine learning (ML)-driven closed-loop system to generate custom AI hardware platforms for the target algorithms and/or performance/energy constraints using a library of lego-like heterogeneous hardware building blocks.

Krishna has been an assistant professor in the Georgia Tech School of Electrical and Computer Engineering (ECE), where he leads the Synergy Lab. He and his team focus on architecting next-generation intelligent computer systems and interconnection networks for emerging application areas such as machine learning. Krishna has received a Google Faculty Research Award and an NSF CISE Research Initiation Initiative Award. He recently had one of his papers selected as an IEEE Micro Top Pick from computer architecture conferences and a second paper was selected as an Honorable Mention; Krishna’s work will be acknowledged in the May/June 2019 issue of IEEE Micro.