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Articles 1 - 4 of 4
Full-Text Articles in Computer Engineering
Adversarial Patch Attacks On Deep Reinforcement Learning Algorithms, Peizhen Tong
Adversarial Patch Attacks On Deep Reinforcement Learning Algorithms, Peizhen Tong
McKelvey School of Engineering Theses & Dissertations
Adversarial patch attack has demonstrated that it can cause the misclassification of deep neural networks to the target label when the size of patch is relatively small to the size of input image; however, the effectiveness of adversarial patch attack has never been experimented on deep reinforcement learning algorithms. We design algorithms to generate adversarial patches to attack two types of deep reinforcement learning algorithms, including deep Q-networks (DQN) and proximal policy optimization (PPO). Our algorithms of generating adversarial patch consist of two parts: choosing attack position and training adversarial patch on that position. Under the same bound of total …
Applying Hls To Fpga Data Preprocessing In The Advanced Particle-Astrophysics Telescope, Meagan Konst
Applying Hls To Fpga Data Preprocessing In The Advanced Particle-Astrophysics Telescope, Meagan Konst
McKelvey School of Engineering Theses & Dissertations
The Advanced Particle-astrophysics Telescope (APT) and its preliminary iteration the Antarctic Demonstrator for APT (ADAPT) are highly collaborative projects that seek to capture gamma-ray emissions. Along with dark matter and ultra-heavy cosmic ray nuclei measurements, APT will provide sub-degree localization and polarization measurements for gamma-ray transients. This will allow for devices on Earth to point to the direction from which the gamma-ray transients originated in order to collect additional data. The data collection process is as follows. A scintillation occurs and is detected by the wavelength-shifting fibers. This signal is then read by an ASIC and stored in an ADC …
Investigating Single Precision Floating General Matrix Multiply In Heterogeneous Hardware, Steven Harris
Investigating Single Precision Floating General Matrix Multiply In Heterogeneous Hardware, Steven Harris
McKelvey School of Engineering Theses & Dissertations
The fundamental operation of matrix multiplication is ubiquitous across a myriad of disciplines. Yet, the identification of new optimizations for matrix multiplication remains relevant for emerging hardware architectures and heterogeneous systems. Frameworks such as OpenCL enable computation orchestration on existing systems, and its availability using the Intel High Level Synthesis compiler allows users to architect new designs for reconfigurable hardware using C/C++. Using the HARPv2 as a vehicle for exploration, we investigate the utility of several of the most notable matrix multiplication optimizations to better understand the performance portability of OpenCL and the implications for such optimizations on this and …
Development Of Scalable Simulator For Spiking Neural Network, Jae Sang Ha
Development Of Scalable Simulator For Spiking Neural Network, Jae Sang Ha
McKelvey School of Engineering Theses & Dissertations
A neural network simulator for Spiking Neural Network (SNN) is a useful research tool to model brain functions with a computer. With this tool, different parameters can be explored easily compared to using a real brain. For several decades, researchers have developed many software packages and simulators to accelerate research in computational neuroscience. However, despite their advantages, different neural simulators possess different limitations, such as flexibility of choosing different neuron models and scalability of simulators for large numbers of neurons. This paper demonstrates an efficient and scalable spiking neural simulator that is based on growth transform neurons and runs on …