A neural online triggering system based on parallel processing

Author

Seixas, J.M. ; Caloba, L.P. ; Anjos, A.R. ; Kastrup, B. ; Dantas, A.C.H. ; Linhares, R.

Author_Institution

COPPE/EE, Univ. Fed. do Rio de Janeiro, Brazil

Volume

45

Issue

4

fYear

1998

fDate

8/1/1998 12:00:00 AM

Firstpage

1814

Lastpage

1818

Abstract

The study of a prototype of the second-level triggering system for operation at LHC conditions is addressed by means of a parallel machine implementation. The 16 node transputer based machine uses a fast digital signal processor acting as a coprocessor for optimizing signal processing applications. A C-language development environment is used for running all applications at ultimate speed. The implementation is based on information supplied by four detectors and includes two phases of system operation: feature extraction and global decision. Feature extraction for calorimeters and global decision processing are performed by means of neural networks. Preprocessing and neural network parameters rest in memory and the activation function is implemented using a look up table. Simulated data for the second-level trigger operation are used for performance evaluation

Keywords

feature extraction; high energy physics instrumentation computing; neural nets; nuclear electronics; parallel machines; C-language development environment; activation function; fast digital signal processor; feature extraction; global decision; neural network parameters; neural networks; neural online triggering system; parallel machine implementation; parallel processing; performance evaluation; second-level trigger operation; second-level triggering system; signal processing applications; transputer based machine; Coprocessors; Digital signal processing; Digital signal processors; Feature extraction; Large Hadron Collider; Neural networks; Parallel machines; Parallel processing; Phase detection; Prototypes;

fLanguage

English

Journal_Title

Nuclear Science, IEEE Transactions on

Publisher

ieee

ISSN

0018-9499

Type

jour

DOI

10.1109/23.710942

Filename

710942