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 …

Compound Effects Of Clock And Voltage Based Power Side-Channel Countermeasures, Jacqueline Lagasse

Masters Theses

The power side-channel attack, which allows an attacker to derive secret information from power traces, continues to be a major vulnerability in many critical systems. Numerous countermeasures have been proposed since its discovery as a serious vulnerability, including both hardware and software implementations. Each countermeasure has its own drawback, with some of the highly effective countermeasures incurring large overhead in area and power. In addition, many countermeasures are quite invasive to the design process, requiring modification of the design and therefore additional validation and testing to ensure its accuracy. Less invasive countermeasures that do not require directly modifying the system …

An Fpga Based Implementation Of The Exact Stochastic Simulation Algorithm, Phani Bharadwaj Vanguri

Masters Theses

Mathematical and statistical modeling of biological systems is a desired goal for many years. Many biochemical models are often evaluated using a deterministic approach, which uses differential equations to describe the chemical interactions. However, such an approach is inaccurate for small species populations as it neglects the discrete representation of population values, presents the possibility of negative populations, and does not represent the stochastic nature of biochemical systems. The Stochastic Simulation Algorithm (SSA) developed by Gillespie is able to properly account for these inherent noise fluctuations. Due to the stochastic nature of the Monte Carlo simulations, large numbers of simulations …