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Optimization Of Ultra-Low Power Application-Specific Asynchronous Deep Learning Integrated Circuit Design, Cole Sherrill
Optimization Of Ultra-Low Power Application-Specific Asynchronous Deep Learning Integrated Circuit Design, Cole Sherrill
Computer Science and Computer Engineering Undergraduate Honors Theses
The Internet of Things (IoT) consists of all devices connected to the internet, including battery-powered devices like surveillance cameras and smart watches. IoT devices are often idle, making leakage power a crucial design constraint. Currently, there are only a few low-power application-specific processors for deep learning. Recently, the Trustable Logic Circuit Design (TruLogic) laboratory at the UofA designed an asynchronous Convolutional Neural Network (CNN) system. However, the original design suffered from delay-sensitivity issues undermining its reliable operation. The aim of this thesis research is to modify the existing CNN circuit to achieve increased reliability and to optimize the improved design …
Prototyping A Capacitive Sensing Device For Gesture Recognition, Chenglong Lin
Prototyping A Capacitive Sensing Device For Gesture Recognition, Chenglong Lin
Computer Science and Computer Engineering Undergraduate Honors Theses
Capacitive sensing is a technology that can detect proximity and touch. It can also be utilized to measure position and acceleration of gesture motions. This technology has many applications, such as replacing mechanical buttons in a gaming device interface, detecting respiration rate without direct contact with the skin, and providing gesture sensing capability for rehabilitation devices. In this thesis, an approach to prototype a capacitive gesture sensing device using the Eagle PCB design software is demonstrated. In addition, this paper tested and evaluated the resulting prototype device, validating the effectiveness of the approach.