All seminars will take place on Fridays at 11 a.m. in DBH 6011. Check seminar details below.
Hung-Wei Tseng
UCR
November 18, 2022
11:00am - 12:00pm
Title:
The upcoming revolution of general-purpose computing
Abstract:
The significance of artificial intelligence (AI) and machine learning (ML) applications has changed the landscape of computer systems: AI accelerators start to emerge in a wide range of devices, from mobile phones to data center servers. In addition to the direct contribution of performance gain in AI/ML workloads, the introduction of AI/ML accelerators bring a new flavor of computation model, matrix processing model, that any matrix-based algorithm can leverage in theory. However, the highly application-specific designs of these accelerators place hurdles for a wider spectrum of workloads.
In this talk, Hung-Wei will discuss state-of-the-art AI/ML accelerators. By transforming existing algorithms to AI/ML-specific functions, Hung-Wei’s research group has demonstrated that we can already achieve 2.5x speedup for linear algebra based kernels using edge TPUs and up to 288x speedup for database join operations through using NVIDIA’s tensor cores. compared with modern CPUs. If we can extend the design of AI/ML accelerators to support more matrix operations, a set of matrix applications, including dynamic programming based algorithms, can achieve more than 10x speedup over conventional GPUs. Finally, Hung-Wei will discuss some of the potential extensions that are essential to make the upcoming revolution of general-purpose computing successful.
In this talk, Hung-Wei will discuss state-of-the-art AI/ML accelerators. By transforming existing algorithms to AI/ML-specific functions, Hung-Wei’s research group has demonstrated that we can already achieve 2.5x speedup for linear algebra based kernels using edge TPUs and up to 288x speedup for database join operations through using NVIDIA’s tensor cores. compared with modern CPUs. If we can extend the design of AI/ML accelerators to support more matrix operations, a set of matrix applications, including dynamic programming based algorithms, can achieve more than 10x speedup over conventional GPUs. Finally, Hung-Wei will discuss some of the potential extensions that are essential to make the upcoming revolution of general-purpose computing successful.
Speaker Bio:
Hung-Wei is currently an assistant professor in the Department of Electrical and Computer Engineering at the University of California, Riverside. He is now leading the Extreme Storage & Computer Architecture Laboratory and focusing on accelerating applications through generalized computing on tensor processors, AI/ML accelerators as well as intelligent data storage systems. He is recognized by facebook faculty research award and IEEE Micro "Top Picks from Computer Architecture" in 2020 for his research in accelerating data-intensive applications through revisiting the storage system design. He got his PhD from the Department of Computer Science and Engineering at the University of California, San Diego.