February 13, 2025

Please share this online guest lecture from Prof. Yatish Turakhia, UCSD, which will be happening at 6 PM on 3/5 with the department. It is organized as part of “EE P538A: Matrix to Machine: GPU Hardware Design on FPGA for AI”. Please find the guest lecture/meeting details below.
When: Wednesday, Mar 5, 2025, ⋅ 6 pm (Pacific Time – Los Angeles)
Join Zoom Meeting
https://washington.zoom.us/j/92182888710?pwd=9gm9aJU3xo8558fNGbDmV0KMxQg4XR.1Meeting ID: 921 8288 8710
Passcode: 839697

Title: Domain-Specific Hardware Acceleration of Bioinformatics

Abstract: With the end of Moore’s Law, domain-specific acceleration has become one of the few viable paths to continue scaling the performance and efficiency of computing hardware. In this talk, I will discuss six broad strategies that domain-specific hardware accelerators employ to achieve significant performance and efficiency gains, using bioinformatics accelerators as case studies. I will also highlight how modern GPUs have leveraged some of these strategies in recent years to accelerate both bioinformatics and AI workloads. Finally, I will discuss how my lab is harnessing parallel computing on GPUs and FPGAs to drive massive acceleration in emerging bioinformatics applications, from real-time genomic epidemiology to pangenomics.

Bio: Dr. Yatish Turakhia is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California San Diego (UCSD), with affiliations in the Department of Computer Science and Engineering (CSE) and the Bioinformatics and Systems Biology (BISB) graduate program. Prior to joining UCSD, he was a postdoctoral scholar at the Genomics Institute, UC Santa Cruz. Dr. Turakhia earned his Ph.D. in Electrical Engineering from Stanford University in 2019 and his bachelor’s and master’s degrees in Electrical Engineering from the Indian Institute of Technology (IIT) Bombay in 2014. He is a recipient of the MIT Technology Review’s Innovators Under 35 award, Hellman Fellowship, Jacobs Early Career Award, Amazon Research Award, NVIDIA Graduate Fellowship, and multiple paper awards.