Engineering Commons^™

Evaluation Of Robust Deep Learning Pipelines Targeting Low Swap Edge Deployment, David Carter Cornett

Masters Theses

The deep learning technique of convolutional neural networks (CNNs) has greatly advanced the state-of-the-art for computer vision tasks such as image classification and object detection. These solutions rely on large systems leveraging wattage-hungry GPUs to provide the computational power to achieve such performance. However, the size, weight and power (SWaP) requirements of these conventional GPU-based deep learning systems are not suitable when a solution requires deployment to so called "Edge" environments such as autonomous vehicles, unmanned aerial vehicles (UAVs) and smart security cameras.

The objective of this work is to benchmark FPGA-based alternatives to conventional GPU systems that have the …

High Frequency Injection Sensorless Control For A Permanent Magnet Synchronous Machine Driven By An Fpga Controlled Sic Inverter, Jared Walden

Masters Theses

As motor drive inverters continue to employ Silicon Carbide (SiC) and Gallium Nitride (GaN) devices for power density improvements, sensorless motor control strategies can be developed with field-programmable gate arrays (FPGA) to take advantage of high inverter switching frequencies. Through the FPGA’s parallel processing capabilities, a high control bandwidth sensorless control algorithm can be employed. Sensorless motor control offers cost reductions through the elimination of mechanical position sensors or more reliable electric drive systems by providing additional position and speed information of the electric motor. Back electromotive force (EMF) estimation or model-based methods used for motor control provide precise sensorless …