Speaker
Description
The storage, transmission and processing of data is a major challenge across many fields of physics and industry. Traditional generic data compression techniques are lossless, but are limited in performance and require additional computation.
BALER [1,2] is an open-source autoencoder-based framework for the development of tailored lossy data compression models suitable for data from multiple disciplines. BALER models can also be used in FPGAs to compress live data from detectors or other sources, potentially allowing for massive increases in network throughput.
BALER is developed by a cross-disciplinary team of physicists, engineers, computer scientists and industry professionals, and has received substantial contributions from a large number of master’s and doctoral students. BALER has received support from industry both in providing datasets to develop BALER, and to transfer industry best practices.
This presentation will introduce BALER, demonstrate its performance on a range of data types, discuss the involvement of students and industry in the project and lessons learned, and include a live demonstration.
[1] https://arxiv.org/pdf/2305.02283.pdf
[2] https://github.com/baler-collaboration/baler
