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.
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Latest News
Congratulations to Atefeh Sohrabizadeh, Cody Hao Yu, Jason Cong from UCLA and Min Gao from Falcon Computing for receiving the 2023 Best Paper Award from the ACM Transactions on Design Automation of Electronic Systems (TODAES) for their paper entitled "AutoDSE: Enabling Software Programmers to...
Congratulations to Prof. Jason Cong and VAST Lab alumni, Yiping Fan, Guoling Han, Zhiru Zhang for being the Class of 2023 Inductees to the TCFPGA Hall of Fame for the paper “Application-Specific Instruction Generation for Configurable Processor Architectures” (February 2004).
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Congratulations to Prof. Jason Cong for receiving the “Global Industry Leader” Award from ChipEx’2023 on May 8, 2023 in Tel Aviv, Israel, for “groundbreaking research and development which revolutionized electronic design automation and FPGA design methods, for co-founding several chip design...
Latest Publications
Our Projects
Domain-specific accelerators (DSAs) have shown to offer significant performance and energy efficiency over general-purpose CPUs to meet the ever increasing performance needs. However, it is well-known that the DSAs in field-programmable gate-arrays (FPGAs) or application specific integrated...
Description:
- Compilation in quantum computing (QC)
- Optimality study - how far are we from optimal?
- Optimal quantum layout synthesis
- Exploring architecture design with layout synthesis
- Layout synthesis for reconfigruable QC...
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 set of works, we are exploring learning algorithms and acceleration techniques on graph learning algorithms. At their core, they deal with sparse...
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
Optimal Layout Synthesizer of Quantum Circuits for Dynamically Field-Programmable Qubits Array. https://github.com/UCLA-VAST/DPQA
Open-source repository: https://github.com/jshinnerl/pekoMS_2006_book
The generating algorithm is described in https://doi.org/10.1007/978-0-387-68739-1_2