Description
This thesis presents efficient implementations of several linear algebra kernels, machine learning kernels and a neural network based recommender systems engine onto a massively parallel reconfigurable architecture, Transformer. The linear algebra kernels include Triangular Matrix Solver (TRSM), LU Decomposition (LUD), QR Decomposition (QRD), and Matrix Inversion.
Download count: 0
Details
Contributors
- Soorishetty, Anuraag (Author)
- Chakrabarti, Chaitali (Thesis advisor)
- Kim, Hun Seok (Committee member)
- LiKamWa, Robert (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2019
Subjects
Resource Type
Collections this item is in
Note
- Masters Thesis Electrical Engineering 2019