Current projects

Title Description Faculty
Acceleration of Deep Learning for Cloud and Edge Computing

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 framework to accelerate the full stack of convolutional neural networks (CNN), including both convolutional layers and fully-connected layers. Following this work, we further explore the efficient microarchitecture for implementing the computation-intensive kernels in CNN. A special architecture,...

Jason Cong
Architecture and Compilation for Quantum Computing

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.

Jason Cong
Customizable Domain-Specific Computing

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 processor; and hundreds to thousands of computing servers connected in warehouse-scale data centers. However, such highly parallel, general-purposecomputing systems still face serious challenges in terms of performance, energy, heat dissipation, space, and cost. In this project we look beyond...

Jason Cong
Customized Computing for Big-Data Applications

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 requires great sequencing depths, structural aberrations are difficult to detect with today’s algorithms, and the tumor has the ability to evolve, meaning the same tumor might be assayed a great many times during the course of treatment.  The goal of this research project is to make apply the domain-...

Jason Cong
Customized Computing for Brain Research and Brain-Inspired Computing

Direction 1: Real-Time Neural Signal Processing for Closed-Loop Neurofeedback Applications. 

Recent work in this project got the Best Paper Award in ISLPED'18, and Pearl Cohen Poster Award in 2019 UCLA Bioscience Innovation Day.

The miniaturized fluorescence microscope (Miniscope) and the tetrodes assembly are emerging techniques in...
Jason Cong
Near Data Computing

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 movement is proved to be very expensive which can no longer be ignored in the Big Data era. To meet the ever-increasing performance need, we expect the computer system to be redesigned in the data-centric fashion. Different computing engines are deployed in different storage hierarchies, including cache,...

Jason Cong
Programming Infrastructure for Heterogeneous Architectures

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 platforms, such as the CPU+FPGA multi-chip packages by Intel and the GPU and FPGA enabled AWS cloud by Amazon. However, although these heterogeneous hardware computing platforms are becoming widely available to the industry, they are very difficult to program especially with FPGAs. The use of such...

Jason Cong