A PROGRAMMABLE ACTIVE MEMORY IMPLEMENTATION OF A NEURAL NETWORK FOR SECOND LEVEL TRIGGERING IN ATLAS

Lars Lundheim, Iosif Legrand and Laurent Moll
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Lars Lundheim: CERN, European Organisation for Nuclear Research, CH-1211 Geneva 23, Switzerland
Iosif Legrand: DESY-IFH, PLATAVENALLEE 6, 15738 Zeuthen, Germany
Laurent Moll: Pôle Universitaire Léonard de Vinci, 83 avenue François Arago, 92017 Nanterre Cedex, France

International Journal of Modern Physics C (IJMPC), 1995, vol. 06, issue 04, 561-566

Abstract: In the second level triggering for ATLAS “Regions of Interest” (RoIs) are defined in (etha, phi) corresponding to possibly interesting particles. For each RoI physically meaningful parameters are extracted for each subdetector. Based on these parameters a classification of the particle type is made. A feed-forward neural net with 12 input variables, a 6-node intermediate layer, and 4 output nodes has earlier been suggested for this task.The reported work consists of an implementation of this neural net using a DECPeRLe-1, a Programmable Active Memory (PAM). This is a reconfigurable processor based on Field Programmable Gate Arrays (FPGAs), which has also been used for real-time implementation of feature extraction algorithms for second level triggering.The implementation is pipelined, runs with a clock of 25 MHz, and uses 0.64 microseconds for one particle classification. Integer arithmetic is used, and the performance is comparable to a floating point version.

Date: 1995
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DOI: 10.1142/S0129183195000435

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