Tushar Krishna is an Associate Professor (with tenure) in the School of Electrical and Computer Engineering at Georgia Tech. He is currently also a Visiting Associate Professor at the School of Electrical Engineering and Computer Science at MIT. He held the ON Semiconductor (Endowed) Junior Professorship in the School of ECE at Georgia Tech from 2019-2021. He has a Ph.D. in Electrical Engineering and Computer Science from MIT (2014), a M.S.E in Electrical Engineering from Princeton University (2009), and a B.Tech in Electrical Engineering from the Indian Institute of Technology (IIT) Delhi (2007). Before joining Georgia Tech in 2015, Dr. Krishna spent a year as a researcher at the VSSAD group at Intel, Massachusetts.

Dr. Krishna’s research spans computer architecture, interconnection networks, networks-on-chip (NoC), and AI/ML accelerator systems – with a focus on optimizing data movement in modern computing platforms. His research is funded via multiple awards from NSF, DARPA, IARPA, SRC (including JUMP2.0), Department of Energy, Intel, Google, Meta/Facebook, Qualcomm and TSMC.

Dr. Suvinay Subramanian is a computer architect at Google, building hardware systems (TPUs) to accelerate machine learning and AI. His expertise is in hardware-software codesign and has worked on solutions spanning the hardware-software stack ranging from circuits, microarchitecture, architecture, programming models, and interconnection networks.

He received a Ph.D. from MIT (CSAIL), advised by Daniel Sanchez. He helped develop new programming models and multi-core architectures to exploit challenging forms of parallelism in applications. He completed his master's at MIT advised by Li-Shiuan Peh on high-performance interconnection networks. Prior to MIT, He did his undergraduate at IIT Madras.

Kwon, a research scientist at Meta’s (formerly Facebook) Reality Labs, works on deep learning accelerators with flexible dataflow and mappings based on data- and communication-centric approaches. Kwon earned his doctorate in computer science and while at Georgia Tech, he developed a flexible deep neural network accelerator called MAERI and an open-source infrastructure for modeling dataflows within deep learning accelerators called MAESTRO. His thesis was recognized with an honorable mention at the 2021 Outstanding Dissertation Award competition of the Association for Computing Machinery's Special Interest Group on Computer Architecture/IEEE Computer Society’s Technical Committee on Computer Architecture. He is co-author of a book on computer architecture and has published 25 peer-reviewed articles.

Dr. Ananda Samajdar is a Research Staff Member at IBM T.J. Watson Research Center in Yorktown Heights. He joined IBM in 2022 after obtaining my PhD from Georgia Institute of Technology. He was advised by Dr. Tushar Krishna. Heis currently exploring accelerator design, and compilation/mapping strategies for DNN workloads on IBM's RaPiD AI accelerator. During his Ph.D., he developed SCALE-SIM, a cycle-accurate systolic accelerator simulator.

Abhimanyu Bambhaniya is a Ph.D. student in the School of Electrical and Computer Engineering at Georgia Tech advised by Dr. Tushar Krishna. He has a B.Tech. in Electronics and Communication Engineering from the Indian Institute of Technology (IIT) Roorkee (2019). Before joining Georgia Tech worked on taping out an 8TOPS edge AI accelerator. His research spans deep learning accelerators and algorithms, interconnection networks, networks-on-chip (NoC), and computer architecture with a special focus on accelerating and optimizing attention-based models. 

Hao Kang received his BTech in Computer Science(CS) from Zhejiang University(ZJU) in 2023. He Joined Georgia Tech as a SCS Ph.D. student in Fall2023. His research interests include model compression, ML model profilers/simulators, and Mlsys for special models(e.g. LLM) and systems(e.g. quantum system).