Abstract: Several real-world applications of machine learning (ML) systems such as robotics, autonomous cars, assistive technologies, smart manufacturing, and many other Internet-of-Things (IoT) applications require real-time inference with low energy consumption. The surge in demand for specialized hardware for AI applications has resulted in a rapidly expanding industry for edge AI accelerators. Anticipating this trend, several companies have developed their own specialized accelerators such as the NVIDIA Jetson Nano, Intel NCS2, and Google TPU. While many conventional neural networks can be readily deployed on many of these platforms, the support for deploying more advanced and larger models such as transformers on them has yet to be researched and developed. In this talk, we discuss two of our recent projects in which we utilize optimization mechanism such neural architecture search (NAS) and system-level innovations such as modifying the computational graphs, partitioning, and refactoring the unsupported operations to efficiently deploy ML models on edge accelerators for computer vision and natural language processing tasks.

Bio: Dr. Ramtin Zand is an assistant professor of the Computer Science and Engineering and the principal investigator of the Intelligent Circuits, Architectures, and Systems (iCAS) Lab at the University of South Carolina. The iCAS lab has close collaborations with and is supported by several multinational companies including Intel, AMD, and Juniper Networks, as well as federal agencies such as National Science Foundation (NSF). Dr. Zand has authored more than 50 journal and conference articles and two book chapters and received recognition from ACM/IEEE including the best paper runner-up of ACM GLSVLSI’18, the best poster of ACM GLSVLSI’19, and best paper of IEEE ISVLSI’21, as well as featured paper in IEEE Transactions on Emerging Topics in Computing. He has received the NSF CAREER award in 2024. His research focus is on neuromorphic computing, edge computing, processing-in-memory, and AI/ML hardware acceleration.

Online at: https://us06web.zoom.us/j/8440139296?pwd=b09lRCtJR0FCTWcyeGtCVVlUMDNKQT09&omn=85050122519