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 21, 2018 | 0 comments

Congratulations to Deming Chen, a former member of Prof. Cong's lab, for being elected to IEEE fellow "for contributions to FPGA high-level synthesis."

The IEEE is the world’s leading professional association for advancing technology for...

November 21, 2018 | 0 comments

Prof. Cong delivered a keynote speech at the 2018 Computing in the 21st Century Conference (21CCC) & Asia Faculty Summit hosted by Microsoft Research Asia (MSRA) in junction with the MSRA 20-Year Anniversary Celebration on November 6, 2018...

August 1, 2018 | 0 comments

The paper is co-authored by Zhe Chen, Andrew Howe, Hugh T. Blair, and Jason Cong with the title  "CLINK: Compact LSTM Inference Kernel for Energy Efficient Neurofeedback Devices.” received the Best Paper Award at the International Symposium on...

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]: 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
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
[PDF]: 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]: Customizable Computing– From Single Chip to Datacenters
Journal publication
Jason Cong, Zhenman Fang, Muhuan Huang, Peng Wei, Di Wu, and Cody Hao Yu
[PDF]: Unleash The Performance of Emerging Storage via Reconfigurable Drive Controller
Conference publication
Zhenyuan Ruan, Tong He, Jason Cong
[PDF]: Caffeine: Towards Uniformed Representation and Acceleration for Deep Convolutional Neural Networks
Journal publication
Chen Zhang, Guangyu Sun, Zhenman Fang, Peipei Zhou, Peichen Pan, 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 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.