Design and evaluation of an FPGA online feature extraction data pre-processing stage for the CBM-TRD experiment

Abstract

Feature extraction is a data pre-processing stage of the Transition Radiation Detector (TRD) data-acquisition chain (DAQ) as part of the Compressed Baryonic Matter (CBM) experiment. The feature extraction stage delivers event-filtered and bandwidth-reduced data to the First Level Event Selector (FLES). The feature extraction stage implements multiple processing algorithms in order to find and extract regions of interest within time series signals. Algorithms such as peak-finding, signal integration, center of gravity and time-over threshold were implemented for online analysis. On the other hand, a local clustering algorithm allows to find cluster members and to implement even further data reduction algorithms. A feature extraction framework for automatic firmware generation has been tested for the CBM-TRD data acquisition chain. The framework allows the generation of Field Programmable Gate Array (FPGA) designs that implement feature extraction algorithms. Such designs are FPGA-platform independent and are described by a file written in a Domain Specific Language (DSL). The result of using the mentioned feature extraction framework for the TRD feature extraction stage is presented and discussed.