Work

Decision tree-based anomaly detection on FPGA

Year: 2025

Type: conference-paper

Abstract: Anomaly detection using a decision trees on field programmable gate array, FPGA, is discussed. The decision tree-based autoencoder is trained using importance sampling of the vector space of input variables. Using LHC-inspired simulated data samples inference is made at 30 ns using minimal resource utilization, which is competitive to the neural network-based models, such as variational autoencoders, that are simplified to implement on FPGA-based real-time trigger systems.

Source: International Conference on High Energy Physics (ICHEP)

DOI: 10.22323/1.476.1058

Author: Tae Min Hong

Institution: University of Pittsburgh

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: U.S. Department of Energy

Award: PARTICLE PHYSICS AT THE UNIVERSITY OF PITTSBURGH