Machine Learning on FPGAs: acceleration of CNNs and other large-scale algorithms

http://photos4.meetupstatic.com/photos/event/8/5/e/d/600_444094285.jpeg

Speaker: Jason Cong is a Chancellor’s Professor at the UCLA Computer Science Department, the director of Center for Domain-Specific Computing (CDSC), and the CS Department Chair from 2005 to 2008.

He has over 400 publications, including 10 best paper awards, and the 2011 ACM/IEEE A. Richard Newton Technical Impact Award in Electric Design Automation.

He was elected to an IEEE Fellow in 2000 and ACM Fellow in 2008.

He is the recipient of the 2010 IEEE Circuits and System Society Technical Achievement Award "For seminal contributions to electronic design automation, especially in FPGA synthesis, VLSI interconnect optimization, and physical design automation."

Dr. Cong’s research interests include electronic design automation, energy-efficient computing, customized computing for big-data applications, and highly scalable algorithms.

Dr. Cong has successfully co-founded three companies with his students, including Aplus Design Technologies for FPGA physical synthesis and architecture evaluation (acquired by Magma in 2003, now part of Synopsys), AutoESL Design Technologies for high-level synthesis (acquired by Xilinx in 2011), and Neptune Design Automation for ultra-fast FPGA physical design (acquired by Xilinx in 2013).

Currently, he is a co-founder and the chief scientific advisor of Falcon Computing Solutions, a startup dedicated to enabling FPGA-based customized computing in data centers. Dr. Cong is also a distinguished visiting professor at Peking University.

Abstract: Machine learning has become one of the most important workloads in datacenter computing. In this talk, I shall present our recent research on acceleration various machine learning algorithms on FPGAs. I shall first present that results on accelerating image detection and recognition based on CNNs (convolutional neural networks). Then, I shall discuss accelerating large-scale machine learning algorithms, such as logistic regression and K-mean clustering, in clustered computing environment using SPARK. Some of the results are obtained in collaboration with Falcon Computing Solutions, Inc., a startup dedicated to enabling FPGA-based customized computing in data centers.

Link to Professor Jason Cong lab at UCLA: http://vast.cs.ucla.edu/

Location: Baidu Research

1195 Bordeaux Drive, Sunnyvale, CA

Please enter through front lobby. Meeting will be in Baidu Cafe.

Agenda:

6:00pm - Refreshments and networking

6:15-7:15pm - Machine Learning on FPGAs

7:15-8:00pm - Networking

This meeting is generously sponsored by Baidu Research.