Dual-Triangular QR Decomposition with Global Acceleration and Partially Q-Rotation Skipping
Journal
FPT 2022 - 21st International Conference on Field-Programmable Technology, Proceedings
ISBN
9781665453363
Date Issued
2022-01-01
Author(s)
Abstract
Efficient matrix operations have been deemed keys to efficient data analysis. Dual-Triangular QR Decomposition (DT-QRD) is a critical component in Tall and skinny QR decomposition (TS-QRD), which is a widely-used matrix operation with various applications, such as data compression and feature extraction. In order to accelerate DT-QRD, in this paper, we propose a new acceleration framework, including Global Acceleration Schemes, and Partially boldsymbol{Q} -rotation Skipping, which utilize the special DT structure in both mathbf{Q} and mathbf{R} matrix to reduce the latency and computation resource. Further, we employ the Systolic-Array Based Architecture (1D & 2D) for implementation to reduce the memory usage. Experimental results manifest that our framework achieves 169.70 times ( mathbf{1} mathbf{D}) and 250.13 times ( mathbf{2} mathbf{D}) speedup.
Subjects
Dual-triangular QR decomposition | High-Level Synthesis | Systolic Array
Type
conference paper