SHF:Small: Tensor-Based Algorithm and Hardware Co-Optimization for Neural Network Architecture

University Of California-Santa Barbara, Santa Barbara CA

Investigators

Abstract

Machine learning plays an important role in our daily life, including medical data analysis, finance, autonomous driving and computer vision. Many popular machine learning models are both data-intensive and computationally expensive. In order to address this challenge, algorithm and architecture co-design and co-optimizations are required to achieve better performance and energy efficiency. This project aims to develop a more powerful learning architecture by simultaneously optimizing the neural network algorithm and its hardware implementation. The project will have a broad impact. The AI applications will gain a significant performance boost if the computational and storage cost of its algorithm can be reduced significantly. It will improve many applications such as data mining, medical imaging analysis, computational biology, and financial analysis. The project will greatly enrich the undergraduate and graduate course curriculum and attract graduate and undergraduate students to participate in this project and related workshops. Finally, this project will develop new AI, VLSI and computer architecture workforce with solid background in several areas including computational math, machine learning, and hardware design. Tensors are a generalization of vectors and matrices, and they are promising tools to represent and numerically process high-dimensional data arrays. Leveraging the high effectiveness of tensor computation in big-data analysis, this project will investigate three specific topics towards designing high-performance and energy-efficient machine learning hardware. First, theoretically sound and novel tensor numerical algorithms will be developed to significantly reduce the training and inference cost of deep learning. Second, the algorithm framework will be optimized on existing hardware (e.g., GPU and FPGA) to achieve better performance, and to examine the main challenges when running on hardware platforms. Finally, emerging design technologies (e.g., 3-D process-in-memory) will be investigated to design specific hardware libraries to perform fundamental tensor computation and to further boost the performance and energy efficiency of the whole machine learning architecture. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.