Title：On System Challenges for Smart Machines

Speaker：Guang R. Gao

ACM Fellow and IEEE Fellow,

Endowed Distinguished Professor (Emeritus)

University of Delaware

Time：6.8 15：00-16：00

Venue：East main-buliding 10-103

Abstract：We are facing the challenges of the end of Moore’s Law, as well as the challenges from applications in Smart Machines – propelled by advances in intelligent data analytics, machine learning and brain-inspired computing models. The speaker believes that it may be the time to initiate a new forum encouraging broader participation and direct interaction of scientists and engineers working on computer system architecture, system software (including compilers, runtime systems, and OS) and high level programming programing models and methodology, as well as scientists who are mapping demanding applications on to future smart machines. The talk will be focused on the software foundation: program composability and scalability in large-scale parallel and distributed systems.

To this end, PTM – a parallel Turing Machine model is discussed as an attempt toward this direction

Bio: Guang R. Gao is a computer scientist and the 2017 Recipient of the IEEE Computer Society B. Ramakrishna Rau Award.

Gao has received his undergraduate education in Tsinghua University in Beijing. He received his Master and Ph.D degree in 1982 and 1986 respectively both in Computer Science at Massachusetts Institute of Technology (MIT) -- the first from mainland China. Gao has devoted his research and academic careers in dataflow model of computation. He has led a series of parallel architecture and system projects where various aspects of dataflow models explored – ranging from innovations in programming paradigms, architecture features, and system software technology.

Gao is an ACM Fellow and IEEE Fellow. He has been awarded an Endowed Named Professorship of Electrical and Computer Engineering at University of Delaware. Gao is a founder and chairman of the IEEE/CS Dataflow STC – the Special Interested Community of Parallel Model and System: Dataflow and Beyond.