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

July 8, 2021 | 0 comments

Prof. Cong gave a keynote speech entitled “Layout Synthesis for Quantum Computing: Gap Analysis and Optimal Solution” at the 2021 IEEE Computer Society Annual Symposium on VLSI...

May 25, 2021 | 0 comments

Prof. Cong gave a keynote speech entitled...

May 18, 2021 | 0 comments

Jie Wang was selected as one of the four winners of the 2021 Outstanding Graduate Student Research Awards by UCLA CS department. Jie is currently a sixth-year PhD student under the supervision of Prof.Jason Cong. He received his bachelor degree...

Latest Publications

Scaling Up Hardware Accelerator Verification using A-QED with Functional Decomposition
Conference publication
Saranyu Chattopadhyay, Florian Lonsing, Luca Piccolboni, Deepraj Soni, Peng Wei, Xiaofan Zhang, Yuan Zhou, Luca Carloni, Deming Chen, Jason Cong, Ramesh Karri, Zhiru Zhang, Caroline Trippel, Clark Barrett and Subhasish Mitra
Fast Calcium Trace Extraction for Large-Field-of-View Miniscope
Conference publication
Zhe Chen, Garrett J. Blair, Hugh T. Blair, Jason Cong
Live Demonstration: Real-Time Calcium Trace Extraction from Large-Field-of-View Miniscope
Conference publication
Zhe Chen, Garrett J. Blair, Changliang Guo, Daniel Aharoni, Hugh T. Blair, Jason Cong
TENET: A Framework for Modeling Tensor Dataflow Based on Relation-centric Notation
Conference publication
Liqiang Lu, Naiqing Guan, Yuyue Wang, Liancheng Jia, Zizhang Luo, Jieming Yin, Jason Cong, Yun Liang
Extending High-Level Synthesis for Task-Parallel Programs
Conference publication
Yuze Chi, Licheng Guo, Jason Lau, Young-kyu Choi, Jie Wang, and Jason Cong
FANS: FPGA-Accelerated Near-Storage Sorting
Conference publication
Weikang Qiao, Jihun Oh, Licheng Guo, Mau-Chung Frank Chang, Jason Cong
MOCHA: Multinode Cost Optimization in Heterogeneous Clouds with Accelerators.
Conference publication
Peipei Zhou, Jiayi Sheng, Cody Hao Yu, Peng Wei, Jie Wang, Di Wu, Jason Cong
AutoSA: A Polyhedral Compiler for High-Performance Systolic Arrays on FPGA
Conference publication
Jie Wang, Licheng Guo, and Jason Cong
HBM Connect: High-Performance HLS Interconnect for FPGA HBM
Conference publication
Young-kyu Choi, Yuze Chi, Weikang Qiao, Nikola Samardzic, and Jason Cong
AutoBridge: Coupling Coarse-Grained Floorplanning and Pipelining for High-Frequency HLS Design on Multi-Die FPGAs
Conference publication
Licheng Guo, Yuze Chi, Jie Wang, Jason Lau, Weikang Qiao, Ecenur Ustun, Zhiru Zhang, Jason Cong

Our Projects

Quantum computing (QC) has been shown, in theory, to hold huge advantages over classical computing. However, there remains many engineering challenges in the implementation of real-world QC applications. In order to devide-and-conquer, we can split the task as below.


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...

Direction 1: Real-Time Neural Signal Processing for Closed-Loop Neurofeedback Applications. 

Recent work in this project got the Best Paper Award in ISLPED'18, and...

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

Many quantum computers have constraints on the connections between qubits. However, a quantum program may not conform to these constraints. Thus, it is necessary to perform 'layout synthesis for quantum computing', LSQC, which transforms quantum programs prior to execution so that the...

We are excited that Xilinx has made the decision to open-source the Merlin compiler under the Apache license. The Merlin compiler was originally developed by the Falcon Computing Solutions, a...