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

February 13, 2020 | 0 comments

Zhe was selected as one of eight recipients of the 2019 Chancellor’s Award for Postdoctoral Research.This award was established in 1998 to recognize the important contributions that postdoctoral scholars make to UCLA’s research mission.

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January 21, 2020 | 0 comments

Prof. Jason Cong gave a keynote speech entitled “ Design Automation for Customizable Computing” at ASP-DAC’2020 on Jan. 20, 2020 in Beijing China. ASP-DAC 2020 is the 25th...

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...

Latest Publications

Crane: Mitigating Accelerator Under-utilization Caused by Sparsity Irregularities in CNNs
Journal publication
Yijin Guan, Guangyu Sun, Zhihang Yuan, Xingchen Li, Ningyi Xu, Shu Chen, Jason Cong, and Yuan Xie
[PDF]: End-to-End Optimization of Deep Learning Applications
Conference publication
Atefeh Sohrabizadeh, Jie Wang, and Jason Cong
[PDF]: HeteroHalide: From Image Processing DSL to Efficient FPGA Acceleration
Conference publication
Jiajie Li, Yuze Chi, and Jason Cong
[PDF]: HeteroRefactor: Refactoring for Heterogeneous Computing with FPGA
Conference publication
Jason Lau*, Aishwarya Sivaraman*, Qian Zhang*, Muhammad Ali Gulzar, Jason Cong, Miryung Kim
FLASH: Fast, ParalleL, and Accurate Simulator for HLS
Journal publication
Young-kyu Choi, Yuze Chi, Jie Wang,and Jason Cong
SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising with Self-supervised Perceptual Loss Network
Journal publication
Meng Li, William Hsu, Xiaodong Xie, Jason Cong, and Wen Gao
Hydro-Seq enables contamination-free high-throughput single-cell RNA-sequencing for circulating tumor cells
Journal publication
Yu-Heng Cheng, Yu-Chih Chen, Eric Lin, Riley Brien, Seungwon Jung, Yu-Ting Chen, Woncheol Lee, Zhijian Hao, Saswat Sahoo, Hyun Min Kang, Jason Cong, Monika Burness, Sunitha Nagrath, Max S. Wicha, and Euisik Yoon
[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

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...

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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...