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
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...
Congratulations to Zhe Chen, under the supervision of Professors Jason Cong and Tad Blair, for receiving the Pearl Cohen Poster Award as the 2nd Place Winner in the Precision Medicine and Imaging Section in the 2019 UCLA Bioscience Innovation Day...
Congratulations to Zhe Chen, under the supervision of Professors Jason Cong and Tad Blair, for receiving the Pearl Cohen Poster Award as the 2nd Place Winner in the Precision Medicine and Imaging Section in the 2019 UCLA Bioscience Innovation Day...
Latest Publications
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:
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...