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

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".



July 11, 2019 | 0 comments

UCLA recently graduated its first Centennial Class.  Congratulations to graduating PhDs Cody Hao Yu and Peipei Zhou from the VAST Lab for receiving the 2019 Computer Science Department Awards in the first Centennial Class. 

Cody Hao Yu...

June 6, 2019 | 0 comments

Computer Science Professor Jason Cong, together with his co-authors Dr. Chen Zhang (former visiting student, now at Microsoft Research Asia), Prof. Guangyu Sun (Peking University), Prof.  Zhenman Fang (former postdoc, now faculty member at Simon...

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
Rapid Cycle-Accurate Simulator for High-Level Synthesis
Conference publication
Yuze Chi, Young-kyu Choi, Jason Cong, and Jie Wang

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

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

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...