Performance of ML-Based Bandwidth Compression on FPGAs
(2024) EITM01 20241Department 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:
http://lup.lub.lu.se/student-papers/record/9169526
- author
- Lilius, Aleko LU
- supervisor
- organization
- course
- EITM01 20241
- year
- 2024
- 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}}, }