Optimize Matrix Multiplication Utilizing OpenCL FPGA Kernel

Optimize Matrix Multiplication Utilizing OpenCL FPGA Kernel

This paper presents an analysis of matrix multiplication utilizing OpenCL, optimization techniques, and a quantitative comparison of matrix multiplication between FPGA and CPU performance based on power consumption, speed and efficiency. We show how algorithm improvements, coupled with specific comp...

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Bibliographic Details
Published in:2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE) pp. 945 - 952
Main Authors: Shaer, Bassam, Stewart, Timothy
Format: Conference Proceeding
Language:English
Published: IEEE 24-07-2023
Subjects:
Artificial intelligence
CPU
FPGA
Kernel
Machine learning
Machine learning algorithms
Matrix Multiplication
OpenCL
Performance evaluation
Performance gain
Pipelines
Power demand
Online Access:Get full text
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Summary:This paper presents an analysis of matrix multiplication utilizing OpenCL, optimization techniques, and a quantitative comparison of matrix multiplication between FPGA and CPU performance based on power consumption, speed and efficiency. We show how algorithm improvements, coupled with specific compilation optimization can improve matrix multiplication performance. Furthermore, we demonstrate the potential for performance gains by utilizing onboard FPGAs to off-load machine learning workloads on edge devices.
DOI:10.1109/CSCE60160.2023.00159