A Neural Network for Global Second Level Trigger - A Real-time Implementation on DecPeRLe-1
Lars Lundheim, J. Legrand, Laurent Moll · CERN Document Server (European Organization for Nuclear Research) · 1995
In the second level triggering for ATLAS \Regions of Interest" (RoIs) are de ned in (etha, phi) corresponding to possibly interesting particles.For every RoI physically meaningful parameters are extracted for each subdetector.Based on these parameters a classi cation 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 classi cation task.The reported work consists of an implementation of this neural net using a DECPeRLe-1, a Programmable Active Memory (PAM).This is a recon gurable 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 classi cation.Integer arithmetic is used, and the performance is comparable to a oating point v ersion.