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

October 5, 2020 | 0 comments

Professor Jason Cong has been appointed as the Volgenau Chair for Engineering Excellence in the Samueli School of Engineering. Prof. Cong joined the UCLA faculty in 1990. He is the Director of Center for Domain-Specific Computing (funded by an...

August 14, 2020 | 0 comments

Two UCLA computer scientists have shown that existing compilers, which tell quantum computers how to use their circuits to execute quantum programs, inhibit the computers’ ability to achieve optimal performance. Specifically, their research has...

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...

Latest Publications

Optimal Layout Synthesis for Quantum Computing
Conference publication
Bochen Tan and Jason Cong
[PDF]: BLINK: Bit-Sparse LSTM Inference Kernel Enabling Efficient Calcium Trace Extraction for Neurofeedback Devices
Conference publication
Zhe Chen, Garrett J. Blair, Hugh T. Blair, Jason Cong
SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising with Self-supervised Perceptual Loss Network
Journal publication
Meng Li, William Hsu, Xiaodong Xie, Jason Cong, and Wen Gao
2019 DAC Roundtable
Journal publication
Giovanni De Micheli, Antun Domic, Massimiliano Di Ventra, Martin Roettler, and Jason Cong
Optimality Study of Existing Quantum Computing Layout Synthesis Tools
Journal publication
Bochen Tan and Jason Cong
[PDF]: A-QED Verification of Hardware Accelerators
Conference publication
Eshan Singh, Florian Lonsing, Saranyu Chattopadhyay, Maxwell Strange, Peng Wei, Xiaofan Zhang, Yuan Zhou, Deming Chen, Jason Cong, Priyanka Raina, Zhiru Zhang Clark Barrett and Subhasish Mitra
[PDF]: Exploiting Computation Reuse for Stencil Accelerators
Conference publication
Yuze Chi and Jason Cong
[PDF]: 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

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...

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

Stencil computation is one of the most important kernels in many application domains such as image processing, solving partial differential equations, and cellular automata. Many of the stencil kernels are...

QUantum Mapping Examples with Known Optimal are a few families of quantum programs, i.e., quantum circuits, that have known optimal...