VAST lab at UCLA

The VAST lab at UCLA investigates cutting-edge research topics at the intersection of VLSI technologies, design automation,  architecture and compiler optimization at multiple scales, from micro-architecture building blocks,  to heterogeneous compute nodes, and scalable data centers.  Current focuses include architecture and design automation for emerging technologies, customizable domain-specific computing with applications to multiple domains, such as imaging processing, bioinformatics, data mining and machine learning.

Latest News

November 12, 2019 | 0 comments

Computer Science Professor Jason Cong, together with his co-authors Zhenyuan Ruan (former student, now at MIT), and Tong He (former student, now at Google), have received the 2019 IEEE/ACM William J. McCalla ICCAD Best Paper Award for their paper...

November 7, 2019 | 0 comments

Jason Cong, Distinguished Chancellor’s Professor of computer science in the UCLA Samueli School of Engineering, has won the 2019 University Research Award from the Semiconductor Industry Association, in collaboration with the Semiconductor...

September 25, 2019 | 0 comments

Prof. Cong delivered the IEEE CEDA distinguished lecture at the University of Illinois at Urbana–Champaign on Monday, September 23, 2019.  The talk title is "Democratize Customizable Computing".

 

 

Latest Publications

[PDF]: Understanding Performance Gains of Accelerator-rich Architectures
Conference publication
Zhenman Fang, Farnoosh Javadi, Jason Cong and Glenn Reinman
[PDF]: INSIDER: Designing In-Storage Computing System for Emerging High-Performance Drive
Conference publication
Zhenyuan Ruan, Tong He, and Jason Cong
[PDF]: Frequency Improvement of Systolic Array-Based CNNs on FPGAs
Conference publication
Jiaxi Zhang, Wentai Zhang, Guojie Luo, Xuechao Wei, Yun Liang, and Jason Cong
[PDF]: Hardware Acceleration of Long Read Pairwise Overlapping in Genome Sequencing: A Race Between FPGA and GPU
Conference publication
Licheng Guo, Jason Lau, Zhenyuan Ruan, Peng Wei, and Jason Cong
HeteroCL: A Multi-Paradigm Programming Infrastructure for Software-Defined Reconfigurable Computing
Conference publication
Yi-Hsiang Lai, Yuze Chi, Yuwei Hu, Jie Wang, Cody Hao Yu, Yuan Zhou, Jason Cong, and Zhiru Zhang
In-Depth Analysis on Microarchitectures of Modern Heterogeneous CPU-FPGA Platforms
Journal publication
Young-kyu Choi, Jason Cong, Zhenman Fang, Yuchen Hao, Glenn Reinman, and Peng Wei
[PDF]: RC-NVM: Dual-Addressing Non-Volatile Memory Architecture Supporting Both Row and Column Memory Accesses
Journal publication
Shuo Li, Nong Xiao, Peng Wang, Guangyu Sun, Xiaoyang Wang, Yiran Chen, Hai (Helen) Li, Jason Cong, Tao Zhang
[PDF]: LANMC: LSTM-Assisted Non-Rigid Motion Correction on FPGA for Calcium Image Stabilization
Conference publication
Zhe Chen, Hugh T. Blair, and Jason Cong

Our Projects

Heterogeneous computing with extensive use of accelerators, such as FPGAs and GPUs, has shown great promise to bring in orders of magnitude improvement in computing efficiency for a wide range of applications. The latest advances in industry have led to highly integrated heterogeneous hardware...

Recent work in this project got the Best Paper Award in ISLPED'18, and Pearl Cohen Poster Award in 2019 UCLA Bioscience Innovation Day.

Moore's law has driven the exponential growth of...

In the Big Data era, the volume of data is exploding, putting forward a new challenge to the existing computer system. Traditionally, the computer system is designed to be computing-centric, in which the data from IO devices are transferred and then processed by the CPU. However, the data...

In this project, we explore efficient algorithms and architectures for state-of-the-art deep learning based applications. In the first work, we are exploring learning algorithms and acceleration techniques on graph learning algorithms. The second work, Caffeine, offers a uniformed...

In the era of big data, many applications present siginificant compuational challenges. For example, in the field of bio-infomatics, the computation demand for personalized cancer treatment is prohibitively high for the general-purpose computing technologies, as tumor heterogeneity...

http://www.cdsc.ucla.edu

To meet ever-increasing computing needs and overcome power density limitations, the computing industry has entered theera of parallelization, with tens to hundreds of computing cores integrated into a single...

Software Releases

INSIDER is an FPGA-based full-stack in-storage computing system: 

https://github.com/zainryan/INSIDER-System

https://github.com/zainryan/EISC

Please click the above link...

HeteroCL is a programming infrastructure composed of a Python-based domain-specific language (DSL) and a compilation flow through close collaboration by research groups led by Prof. Zhiru Zhang at Cornell and Prof. Jason Cong at UCLA. The HeteroCL DSL provides a clean abstraction that decouples...