ORCID
0009-0005-9279-9220
Keywords
accelerator, deep neural network, graph neural network, data reuse, dataflow, sparsity
Abstract
The use of machine learning (ML) is pervasive in numerous application domains, such as autonomous driving, scientific computing, robotics, and among others. However, the continuous growth of ML model complexity and data size is posing unprecedented computation and communication demands on current computing systems, especially in the era of large language models. The problem is further compounded by unstructured data and technology limitations.
To address these challenges, this dissertation research explores novel accelerator designs tailored for a wide range of machine learning applications. First, this research investigates a flexible communication fabric that can enable efficient training for deep learning applications in chiplet-based accelerators. Furthermore, this research explores an efficient accelerator architecture that can dynamically handle irregular sparsity in accelerating graph convolutional neural networks. Furthermore, the dissertation uncovers the extensive intermediate feature data reuse opportunities and their communication bottlenecks in complex graph neural network models. These innovative accelerator designs can deliver high-performance, energy-efficient, and scalable solutions for emerging machine learning workloads and advance their practical deployment at an unprecedented scale.
Completion Date
2024
Semester
Fall
Committee Chair
Zheng, Hao
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Department of Electrical and Computer Engineering
Degree Program
Computer Engineering
Format
Identifier
DP0029054
Language
English
Release Date
12-15-2024
Access Status
Dissertation
Campus Location
Orlando (Main) Campus
STARS Citation
Yin, Lingxiang, "High-Performance, Energy-Efficient, and Scalable Accelerator Design for Emerging Machine Learning Applications" (2024). Graduate Thesis and Dissertation post-2024. 86.
https://stars.library.ucf.edu/etd2024/86
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