Mateus Grellert

This talk explores how the Machine Learning (ML) and Circuits & Systems (CAS) fields can mutually benefit from one another. We begin with an overview of key ML concepts and their underlying computational and architectural requirements. Next, we discuss how advances in CAS have enabled the widespread deployment of ML systems, emphasizing the role of Approximate Computing, Dataflow Architectures, and Neural Network Compression as essential enablers of energy efficient implementations. We conclude by highlighting ongoing challenges and open research directions at the intersection of ML and CAS.

Biography:

Mateus Grellert received the M.Sc. degree in Computer Science from the Federal University of Rio Grande do Sul (UFRGS), Brazil, in 2014, and the Ph.D. degree at the same University in 2018. He is an Assistant Professor at the Federal University of Rio Grande do Sul, Brazil, and part of the Microelectronics Group (GME) of the same University. He has been doing research in embedded systems solutions for more than 10 years. He has over 100 published works including topics like complexity-aware machine learning and image processing, hardware design for machine learning and video processing, approximate computing, memory-aware and energy-aware design, and efficient video-coding systems. His current research interests involve efficient algorithms and architectures for machine learning, as well as efficient architectures for video and image compression in constrained, embedded applications. Prof. Grellert is also the chair of the IEEE Circuits and Systems Society Rio Grande do Sul Chapter (CASS-RS), counselor of the Brazilian Microelectronics Society (SBMICRO), member of the SBC Special Committee on Integrated Circuits Design (CECCI) and a member of the Brazilian Committee on Audio, Image, Multimedia and Hypermedia Coding.