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

February 19, 2014 | 0 comments

In the Xilinx Emerging Technology Symposium (ETS) on Feburary 13rd, 2014, the UCLA team led by Professor Jason Cong are invited to demo FPGA acceleration on a 3-D medical imaging pipeline and an end-to-end system level automation flow called ...

October 12, 2013 | 0 comments

Professor Jason Cong has been selected as the keynote speaker at the 31st IEEE International Conference on Computer Design (ICCD). Jason's speech, entitled "Computing Beyond Processors", was presented on October 8th, 2013.

September 11, 2013 | 0 comments

During DAC'2013, current students and alumnis of VAST lab gathered together.

Latest Publications

[PDF]: A Fully Pipelined and Dynamically Composable Architecture of CGRA
Conference publication
Jason Cong, Hui Huang, Chiyuan Ma, Bingjun Xiao and Peipei Zhou
[PDF]: FPGA Implementation of EM Algorithm for 3D CT Reconstruction
Conference publication
Young-kyu Choi, Jason Cong, and Di Wu
[PDF]: An Efficient Design and Implementation of LSM-Tree based Key-Value Store on Open-Channel SSD
Conference publication
J.Cong, S. Jiang, S. Lin, J. Ouyang, G. Sun, P. Wang, C. Zhang
[PDF]: From Design to Design Automation
Conference publication
Jason Cong
Theory and Algorithm for Generalized Memory Partitioning in High-Level Synthesis
Conference publication
Yuxin Wang, Peng Li, Jason Cong
FPGA Simulation Engine for Customized Construction of Neural Microcircuit
Conference publication
H.T. Blair, J. Cong, D. Wu
Accelerator-rich CMPs: From concept to real hardware
Conference publication
Yu-Ting Chen, Jason Cong, Mohammad Ali Ghodrat, Muhuan Huang, Chunyue Liu, Bingjun Xiao, Yi Zou
Improving Polyhedral Code Generation for High-Level Synthesis
Conference publication
W. Zuo, P. Li, D. Chen, L-N. Pouchet, S. Zhong and J. Cong

Our Projects

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...

In this paper we introduce a novel FPGA architecture with RRAM-based programmable interconnects (FPGA-RPI). Programmable interconnects are the dominant part of FPGA. We use RRAMs to build programmable interconnects, and we optimize their structures by exploiting opportunities that emerge in...

Single event upsets (SEUs) are a source of concern for correct operation of CMOS circuits. The severity of the problem is increased as the transistor size and supply voltage decrease. In the traditional or numerical notion of correctness, every output has to be correct to the last bit. However...

With the increasing of the system complexity, the needs of system level design automation becomes more and more urgent. The maturity of high-level synthesis pushes the desgin abstraction from register-transfer level (RTL) to software programming language like C/C++. However, the state-of-art...

Reconfigurable computing combines the flexibility of software along with the high performance of hardware and exhibits many advantages including flexibility, reduced time-to-market, lower system costs, and capability of adding new features. However, there are a number of drawbacks associated...

Software Releases

CMOST is a system-level design automation framework for FPGA. The main features are:

  • Analyze and extract...

PolyOpt/HLS is a polyhedral loop optimization framework dedicated to data reuse optimization for High-Level Synthesis, integrated in the ROSE compiler. The main features are:

  • Automatic extraction of regions that can be optimized in the polyhedral model
  • Full support of PoCC...

The xPilot Team:

  • Professor Jason Cong
  • Researchers: Deming Chen, Yiping Fan, Guoling Han, Wei Jiang, Bin Liu, Junjuan Xu, Zhiru Zhang