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

April 18, 2019 | 0 comments

Prof. Jason Cong was one of three recipients of the 2019 CESASC Achievement Award,  which was given on Sunday, April 142019 at the 57th Annual Convention of Chinese-American Engineers and Scientists Association...

March 19, 2019 | 0 comments

Prof. Jason Cong is one of the recipients of the Google Faculty Research Award (FRA) for 2019.  Google FRA program is focused on funding world-class technical research in Computer Science, Engineering, and related fields.  Among 910 proposals...

February 27, 2019 | 0 comments

 Computer Science Professor Jason Cong and coauthors Yi-Hsiang Lai, Yuze Chi, Yuwei Hu, Jie Wang, Cody Hao Yu, Yuan Zhou, and Prof. Zhiru Zhang has received the Best Paper Award at the 27th ACM/SIGDA International Symposium on Field-Programmable...

Latest Publications

[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 Cheng, 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
Rapid Cycle-Accurate Simulator for High-Level Synthesis
Conference publication
Yuze Chi, Young-kyu Choi, Jason Cong, and Jie Wang
[PDF]: TGPA: Tile-Grained Pipeline Architecture for Low Latency CNN Inference
Conference publication
Xuechao Wei, Yun Liang, Xiuhong Li, Cody Hao Yu, Peng Zhang, and Jason Cong
[PDF]: A Millimeter-Wave CMOS Transceiver With Digitally Pre-Distorted PAM-4 Modulation for Contactless Communications
Journal publication
Yanghyo Kim, Boyu Hu, Yuan Du, Wei-Han Cho, Rulin Huang, Adrian Tang, Huan-Neng Chen, Chewnpu Jou, Jason Cong, Tatsuo Itoh, and Mau-Chung Frank Chang
[PDF]: Customizable Computing– From Single Chip to Datacenters
Journal publication
Jason Cong, Zhenman Fang, Muhuan Huang, Peng Wei, Di Wu, and Cody Hao Yu

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 from this project got the Best Paper Award in ISLPED'18.

Moore’s law has driven the exponential growth of information technology for more than 50 years, during which the ever-...

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. The first work, Caffeine, offers a uniformed framework to accelerate the full stack of convolutional neural networks (CNN), including both convolutional layers and...

Many applications in precision medicine present significant computational challenges.  For example, the computation demand for personalized cancer treatment is prohibitively high for the general-purpose computing technologies, as tumor heterogeneity requires great sequencing depths,...

With the increasing of the system complexity, the needs of system level design automation becomes more and more urgent. The maturity of high-level synthesis pushes the desgin abstraction from register-transfer level (RTL) to software programming language like C/C++. However, the state-of-art...

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

Cloud-scale BWAMEM (CS-BWAMEM) is an ultrafast and highly scalable aligner built on top of cloud infrastructures, including Spark and Hadoop distributed file system (HDFS). It leverages the abundant computing resources in a public or private cloud to fully exploit the parallelism obtained from...

With the rapid evolution of CPU-FPGA heterogeneous acceleration platforms, it is critical for both platform developers and users to quantify the fundamental microarchitectural features of the platforms. We developed a set of microbenchmarks to evaluate mainstream CPU-FPGA platforms.