Computer 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 …

Side Channel Attack Counter Measure Using A Moving Target Architecture, Jithin Joseph

Electrical and Computer Engineering ETDs

A novel countermeasure to side-channel power analysis attacks called Side-channel Power analysis Resistance for Encryption Algorithms using DPR or SPREAD is investigated in this thesis. The countermeasure leverages a strategy that is best characterized as a moving target architecture. Modern field programmable gate arrays (FPGA) architectures provide support for dynamic partial reconfiguration (DPR), a feature that allows real-time reconfiguration of the programmable logic (PL). The moving target architecture proposed in this work leverages DPR to implement a power analysis countermeasure to side-channel attacks, the most common of which are referred to as differential power analysis (DPA) and correlation power analysis …