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
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...
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...
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...
Latest Publications
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
https://github.com/UCLA-VAST/soda
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...
https://github.com/UCLA-VAST/QUEKO-benchmark
QUantum Mapping Examples with Known Optimal are a few families of quantum programs, i.e., quantum circuits, that have known optimal...