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
Congratulations to VAST Lab members Licheng Guo and Prof. Cong, together with collaborators from AMD/Xilinx and Cornell, Ghent University for winning the Best Paper Award at the 2022 ACM/SIGDA International Symposium on Field-...
Prof. Jason Cong gave the Vision Address at the 35th International Conference on VLSI Design entitled "Democratize IC Designs and Customized Computing" on February 28, 2022 (...
Congratulations to Prof. David Pan for being elected to ACM Fellow for "For contributions to electronic design automation, including design for manufacturing and physical design”.
Dr. Pan...
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...
Direction 1: Real-Time Neural Signal Processing for Closed-Loop Neurofeedback Applications.
The miniaturized fluorescence microscope (Miniscope) and the tetrodes assembly are emerging techniques in observing the activity of a large population of neuros in vivo. It opens up new research...
In the Big Data era, the volume of data is exploding, putting forward a new challenge to existing computer systems. Traditionally, the computer system is designed to be computing-centric, in which the data from IO devices is transferred and then processed by the CPU. However, this data movement...
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
Pyxis collects open-source accelerator designs and the performance data.
Sextans is an FPGA accelerator for general-purpose Sparse-Matrix Dense-Matrix Multiplication (SpMM).
https://github.com/UCLA-VAST/AutoDSE
Adopting FPGA as an accelerator in datacenters is becoming mainstream for customized computing, but the fact that FPGAs are hard to program creates a steep learning curve for software...