Skip to main content

LUP Student Papers

LUND UNIVERSITY LIBRARIES

Performance of ML-Based Bandwidth Compression on FPGAs

Lilius, Aleko LU (2024) EITM01 20241
Department of Electrical and Information Technology
Abstract
This thesis investigates the integration of machine learning (ML)-based compression on Field-Programmable Gate Arrays (FPGAs) to enhance bandwidth compression of data, a crucial aspect in scientific research where large amounts of data are produced in real-time. The compression tool Baler, utilizing autoencoders for ML-based compression, is designed to handle scientific data efficiently. By combining the adaptability of ML models with the computational efficiency of FPGAs, this thesis aims to evaluate the performance of Baler's bandwidth compression. The thesis work reveals that smaller models can effectively fit onto the FPGA, resulting in a throughput increase of 16.9 times compared to a CPU in a desktop computer. This significant... (More)
This thesis investigates the integration of machine learning (ML)-based compression on Field-Programmable Gate Arrays (FPGAs) to enhance bandwidth compression of data, a crucial aspect in scientific research where large amounts of data are produced in real-time. The compression tool Baler, utilizing autoencoders for ML-based compression, is designed to handle scientific data efficiently. By combining the adaptability of ML models with the computational efficiency of FPGAs, this thesis aims to evaluate the performance of Baler's bandwidth compression. The thesis work reveals that smaller models can effectively fit onto the FPGA, resulting in a throughput increase of 16.9 times compared to a CPU in a desktop computer. This significant improvement demonstrates the potential of FPGA-accelerated ML solutions. Key factors influencing optimal FPGA performance, including model size, precision levels, and clock period, were identified. This thesis lays a foundation for further developing hardware implementation of the Baler algorithm, suggesting that the convergence of ML and FPGA technology holds significant potential for enabling more efficient hardware-accelerated ML solutions. (Less)
Popular Abstract
Machine learning is a well-known concept that you probably have heard a lot about. Have you ever wondered how scientists are harnessing the power of machine learning to revolutionize data compression? This thesis explores integrating machine learning-based compression with Field-Programmable Gate Arrays (FPGAs) to enhance bandwidth compression, which is crucial for scientific research where vast amounts of data are generated rapidly in real-time.
Please use this url to cite or link to this publication:
author
Lilius, Aleko LU
supervisor
organization
course
EITM01 20241
year
type
H2 - Master's Degree (Two Years)
subject
keywords
FPGA, Data Compression, Machine Learning, Hls4ml, Baler
report number
LU/LTH-EIT 2024-1005
language
English
id
9169526
date added to LUP
2024-09-02 14:16:49
date last changed
2024-09-02 14:16:49
@misc{9169526,
  abstract     = {{This thesis investigates the integration of machine learning (ML)-based compression on Field-Programmable Gate Arrays (FPGAs) to enhance bandwidth compression of data, a crucial aspect in scientific research where large amounts of data are produced in real-time. The compression tool Baler, utilizing autoencoders for ML-based compression, is designed to handle scientific data efficiently. By combining the adaptability of ML models with the computational efficiency of FPGAs, this thesis aims to evaluate the performance of Baler's bandwidth compression. The thesis work reveals that smaller models can effectively fit onto the FPGA, resulting in a throughput increase of 16.9 times compared to a CPU in a desktop computer. This significant improvement demonstrates the potential of FPGA-accelerated ML solutions. Key factors influencing optimal FPGA performance, including model size, precision levels, and clock period, were identified. This thesis lays a foundation for further developing hardware implementation of the Baler algorithm, suggesting that the convergence of ML and FPGA technology holds significant potential for enabling more efficient hardware-accelerated ML solutions.}},
  author       = {{Lilius, Aleko}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Performance of ML-Based Bandwidth Compression on FPGAs}},
  year         = {{2024}},
}