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Filtering by: Creator Chen, Peter Y. S. Remove constraint Creator: Chen, Peter Y. S. Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science Rights Statement In Copyright Remove constraint Rights Statement: In Copyright

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  • Predicting and Improving Throughput, Responsiveness and Battery Life of Computer Systems by Machine Learning

    Creator:
    Li, Yunfan
    Abstract:
    The Machine Learning (ML) algorithms are increasingly explored in varies of fields including designing and optimizing computer systems. Recent research, such as optimizing memory/cache prefetching by ML training or predicting traffic pattern in throughput processors, also exhibits a promising future of introducing ML into computer system design and optimization. Throughput...
    Resource Type:
    Dissertation
    Full Text:
    approved: Lizhong Chen The Machine Learning (ML) algorithms are increasingly explored in varies of

  • Improving Peak Power Shaving in Data Centers using Deep Neural Networks

    Creator:
    Qi, Fang
    Abstract:
    Data centers have been charged a great amount of electric bill by the power company and demand charge can contribute up to 40% of the electric bill due to the "random" workload. This phenomenon can be avoided by using the existing Uninterrupted Power Supply (UPS) as the assistant power source...
    Resource Type:
    Capstone Project
    Full Text:
    approved: _____________________________________________________ Lizhong Chen Data centers have

  • Design techniques for low-power multi-GS/s analog-to-digital converters

    Creator:
    Jiang, Tao
    Abstract:
    Ultra-high-speed (>10GS/s), medium-resolution (5~6bit), low-power (<50mW) analog-to-digital converter can find it application in the areas of digital oscilloscopes and next-generation serial link receivers. There are several challenges to enable a successful design, however. First, the time-interleaved architecture is required in order to achieve over 10GS/s sampling rate, with the trade-off...
    Resource Type:
    Dissertation
    Full Text:
    : ______________________________________________________ Patrick Y. Chiang Ultra-high-speed (>10GS/s), medium-resolution (5~6bit), low-power

  • Peak Shaving in Data Centers with Deep Reinforcement Learning

    Creator:
    Adhikari, Aashish
    Abstract:
    Data centers have seen a rapid growth in recent years as the technology industry is relying more on cloud services. In effect, data centers are paying millions of US $ in energy costs. Several strategies have been proposed to lower the total energy cost at data centers- task migration, server...
    Resource Type:
    Masters Thesis
    Full Text:
    Centers with Deep Reinforcement Learning Abstract approved: Lizhong Chen Data centers have seen a rapid

  • Machine learning for computer architecture design and optimization

    Creator:
    Penney, Drew
    Abstract:
    Machine learning applied to computer architecture has rapidly transitioned from a theoretical novelty to being a driving force behind design, control, and simulation in practically all components. These machine-learning-based methodologies are further notable for their scalability to increasingly complex design challenges, which has allowed these methodologies to surpass the prior...
    Resource Type:
    Dissertation
    Full Text:
    Chen Machine learning applied to computer architecture has rapidly transitioned from a theoretical

  • Machine Learning for Architectural Design Space Exploration and Resource Control

    Creator:
    Penney, Drew D.
    Abstract:
    Machine learning has enabled significant advancements in diverse fields, yet, with a few exceptions, has had limited impact on computer architecture. Recent work, however, has begun to explore broader application to design, optimization, and simulation. Notably, machine-learning-based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This thesis first...
    Resource Type:
    Masters Thesis
    Full Text:
    Learning for Architectural Design Space Exploration and Resource Control Abstract approved: Lizhong Chen

  • A case for application-based linear transformers : applied towards text-to-spectrogram

    Creator:
    Agostinelli, Victor
    Abstract:
    Various natural language processing (NLP) tasks necessitate deep models that are fast, efficient, and small based on their ultimate application at the edge or elsewhere. While significant investigation has furthered the efficiency and reduced the size of these models, reducing their downstream latency without significant trade-offs remains a difficult task....
    Resource Type:
    Masters Thesis
    Full Text:
    approved: Lizhong Chen Various natural language processing (NLP) tasks necessitate deep models that are

  • Optimizing Interconnection Topology using Deep Reinforcement Learning

    Creator:
    Zeng, Kai
    Abstract:
    As the number of nodes in high-performance computing (HPC) systems continues to grow, it becomes increasingly important to design scalable interconnection network topologies. Prior work has shown promise in adding random shortcuts on top of an existing topology to reduce average hop count and network diameter, but has been limited...
    Resource Type:
    Masters Thesis
    Full Text:
    Topology using Deep Reinforcement Learning Abstract approved: Lizhong Chen As the number of nodes in

  • Three-Dimensional Network-on-Chip Architectures for Cycle Accurate Full-System Simulator

    Creator:
    Agarwal, Akash
    Abstract:
    High Performance Computing can find ubiquitous applications in the industry. HPC-applications are specifically designed to take advantage of the parallel nature of the computing systems which is often enabled by Multi-core/Many-core architectures. With this advent of Multi-processors in the mainstream systems, inter-core communication has been one of the major challenges...
    Resource Type:
    Presentation and Capstone Project
    Full Text:
    . Lizhong Chen Assistant Professor EECS, Oregon State University • Moore: Number of transistors in a

  • Characterizing On-Chip Traffic Patterns in Throughput Processors: A Deep Learning Approach

    Creator:
    Ramamurthy, Abhishek
    Abstract:
    The machine learning and deep learning models have been very lightly explored in analyzing the behavior of On-Chip network traffic. These models have proven their potential in pattern recognition, classification etc... In this paper we analyze the spatial pattern that each workload exhibits in its life cycle during execution. We...
    Resource Type:
    Masters Thesis
    Full Text:
    . Lizhong Chen The machine learning and deep learning models have been very lightly explored in