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

May 18, 2020 | 0 comments

Dr. Young-kyu Choi has received 2020 Cisco Outstanding Graduate Student Research Award for his exceptional research contribution during his PhD studies. He is one of four recipients selected for the honor. His PhD work, "Performance Debugging...

May 6, 2020 | 0 comments

Xilinx Teams with Leading Universities Around the World to Establish Adaptive Compute Research Clusters to spearhead novel research into all areas of adaptive compute acceleration.

The cluster at UCLA will focus on energy-efficient...

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.


Latest Publications

Optimality Study of Existing Quantum Computing Layout Synthesis Tools
Journal publication
Bochen Tan (Daniel) and Jason Cong
Exploiting Computation Reuse for Stencil Accelerators
Conference publication
Yuze Chi and Jason Cong
Analysis and Optimization of the Implicit Broadcasts in FPGA HLS to Improve Maximum Frequency
Conference publication
Licheng Guo, Jason Lau, Yuze Chi, Jie Wang, Cody Hao Yu, Zhe Chen, Zhiru Zhang, and Jason Cong
[PDF]: Bonsai: High-Performance Adaptive Merge Tree Sorting
Conference publication
Nikola Samardzic*, Weikang Qiao*, Vaibhav Aggarwal, Mau-Chung Frank Chang, 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
Algorithm-Hardware Co-design for BQSR Acceleration in Genome Analysis ToolKit
Conference publication
Michael Lo, Zhenman Fang, Jie Wang, Peipei Zhou, Mau-Chung Frank Chang and Jason Cong
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
FLASH: Fast, ParalleL, and Accurate Simulator for HLS
Journal publication
Young-kyu Choi, Yuze Chi, Jie Wang,and Jason Cong

Our Projects

Quantum computing (QC) has been shown, in theory, to hold huge advantage over classical computing. However, there remains many engineering challenges in the implementation of real-world QC applications. One of them, is layout synthesis for quantum computing (LSQC). A quantum program usually...

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...

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

QUantum Mapping Examples with Known Optimal are a few families of quantum programs, i.e., quantum circuits, that have known optimal depths and gate counts for corresponding quantum devices in layout synthesis for quantum computing.