Graduate Research School Available Projects Hardware Implementation of Limited-Precision Machine-Learning Algorithms for Edge Computing
Hardware Implementation of Limited-Precision Machine-Learning Algorithms for Edge Computing
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Title of Project
Hardware Implementation of Limited-Precision Machine-Learning Algorithms for Edge Computing
Advisor/s
Mostafa Rahimi Azghadi
College or Research Centre
College of Science & Engineering
Summary of Project
Machine learning algorithms have shown unprecedented performance in many challenging engineering applications such as machine vision, speech processing, and autonomous learning. These algorithms though, require huge processing capability and consume a high amount of power, which are not available on edge devices in the Internet of Things. This project investigates, design, and implement machine-learning and neuromorphic learning algorithms in hardware to enable edge computing. The developed hardware could be first prototyped on FPGAs and then implemented as an electronic chip to carry out a learning task such as character recognition.
Key Words
machine-learning; neuromorphic computing; IoT; edge computing; FPGA; engineering
Would suit an applicant who
Has an interest in hardware and software design of machine learning algorithms
Updated: 08 Apr 2020