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A Programmable Active Memory Implementation Of A Neural Network For Second Level Triggering In Atlas

Author

Listed:
  • 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)

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.

Suggested Citation

  • Lars Lundheim & Iosif Legrand & Laurent Moll, 1995. "A Programmable Active Memory Implementation Of A Neural Network For Second Level Triggering In Atlas," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 6(04), pages 561-566.
    • Handle: RePEc:wsi:ijmpcx:v:06:y:1995:i:04:n:s0129183195000435
    DOI: 10.1142/S0129183195000435
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