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- Creators: ASU Library. Music Library
- Status: Published
ContributorsDeliwala, Dheeti (Author) / Bryan, Chris (Thesis director) / Strickland, James (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Politics and Global Studies (Contributor)
Created2023-12
ContributorsDeliwala, Dheeti (Author) / Bryan, Chris (Thesis director) / Strickland, James (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Politics and Global Studies (Contributor)
Created2023-12
Description
Vulkan is a modern, low-level, and low-overhead graphics library that allows for the distribution of work across CPU cores using multithreading. This multithreading is possible due to the near full control of the GPU that Vulkan allows. The additional control makes it possible to send multiple instructions to the GPU at the same time. There are a variety of techniques that can be used with Vulkan to effectively improve performance while multithreading instructions to the GPU. One of the challenges of multithreading is the lack of modern-day GPU hardware to support it, which leads to the purpose of this paper, to explore the practicality of multithreading techniques with Vulkan in today’s current computing environment.
ContributorsWahl, Ryan (Author) / Hansford, Dianne (Thesis director) / Kobayashi, Yoshihiro (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-12
ContributorsSeo, YoonJi (Performer) / Choi, Hyeongji (Performer) / Choi, Hyeri (Performer) / Ko, Eunbin (Performer) / Jeon, Dasom (Performer) / Kim, Sungmin (Performer) / Lee, Jiyoung (Performer) / Jeong, Jieun (Performer) / Park, Chulyoung (Performer) / Jo, Hyunsun (Performer) / Steinweg, Tiffany (Performer) / Browning, Natalie (Performer) / ASU Library. Music Library (Publisher)
Created2023-10-28
Description
The goal of this project is to measure the effects of the use of dynamic circuit technology within quantum neural networks. Quantum neural networks are a type of neural network that utilizes quantum encoding and manipulation techniques to learn to solve a problem using quantum or classical data. In their current form these neural networks are linear in nature, not allowing for alternative execution paths, but using dynamic circuits they can be made nonlinear and can execute different paths. We measured the effects of these dynamic circuits on the training time, accuracy, and effective dimension of the quantum neural network across multiple trials to see the impacts of the nonlinear behavior.
ContributorsLynch, Brian (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-12
ContributorsShi, Zhan (Composer) / ASU Library. Music Library (Publisher)
Created2023-10-29
ContributorsHuang, Huatianyuan (Performer) / ASU Library. Music Library (Publisher)
Created2023-10-28
ContributorsKiso, Claudia (Performer) / ASU Library. Music Library (Publisher)
Created2023-10-28
ContributorsPeterson, Danielle (Performer) / Beymanov, Polina (Performer) / ASU Library. Music Library (Publisher)
Created2023-10-29
ContributorsBeymanov, Polina (Performer) / Bolles, Olivia (Performer) / Ho, Ka I (Performer) / Peterson, Danielle (Performer) / Yu, Wan-Ting (Performer) / Salomon, Gabrielle (Performer) / ASU Library. Music Library (Publisher)
Created2023-11-03