Efficient Algorithms and Hardware Accelerators for Tensor Decomposition and Their Applications to Multidimensional Data Analysis


Grant Data
Project Title
Efficient Algorithms and Hardware Accelerators for Tensor Decomposition and Their Applications to Multidimensional Data Analysis
Principal Investigator
Dr Lee, Victor Ho Fun   (Co-principal investigator)
Co-Investigator(s)
Professor Yan Hong   (Project coordinator)
Duration
36
Start Date
2016-06-01
Completion Date
2019-05-31
Amount
360000
Conference Title
Presentation Title
Keywords
Hardware accelerators, Parallel processors, Tensor computing, Tensor decomposition, Numerical analysis
Discipline
Computing Hardware,Applied Mathematics
Panel
Medicine
Sponsor
RGC Collaborative Research Fund (CRF)
HKU Project Code
C1007-15G
Grant Type
Collaborative Research Fund (CRF) - Group Research Project
Funding Year
2015/2016
Status
On-going
Objectives
1. To investigate robust mathematical models for tensor decomposition useful for the analysis of multidimensional data. Project deliverables will include: discovery and proof of theorems, and development of new concepts for tensor models related to eigne-analysis, super-modes and super-compatibilities in high-order structures, existence, orthogonality, uniqueness, and optimality issues. 2. To develop efficient computer algorithms for tensor decomposition, especially those suitable for parallel processors. Project deliverables will include: robust methods for search and optimization, regularization and special tensor structures, generalized projection methods, optimal block structures, scheduling and ordering blocks as input data to parallel processors, combined Jacobi and Gauss-Seidel/SOR iterations, convergence, speed, stability and error bound analysis. 3. To design system architecture and computer software for hardware accelerators for fast tensor computation. Project deliverables will include: mapping tensor computing algorithms to GPU, FPGA and MIC processors using the OpenCL platform, processor specific optimizations, data traffic control and memory management strategies for "Big Data" processing. 4. To apply tensor computing to the analysis of massive multidimensional data, such as those for imaging, video and biomolecular applications. Project deliverables will include: image and video processing and object segmentation and tracking using tensor models, with potential to be realized on low-power and portable devices for commercialization by industry targeting a mass market, extraction of coherent gene expression patterns, and identification of the most affected pathway in lung cancer treatment to combat drug resistance, with a concrete step forward towards personalized medicine.