A VLSI Neural Net Application in High Energy Physics

[LINDSEY@FNAL.FNAL.GOV]

	For those interested in hardware neural network applications,
copies of the following paper are available via mail or fax. Send requests to
Clark Lindsey at BITNET%"LINDSEY at FNAL".
 
            REAL TIME TRACK FINDING IN A DRIFT CHAMBER WITH A 
                            VLSI NEURAL NETWORK*

	Clark S. Lindsey (a), Bruce Denby (a), Herman Haggerty (a),
			    and Ken Johns (b)

	(a) Fermi National Accelerator Laboratory, P.O. Box 500, Batavia, 
	    Illinois 60510.
	(b) University of Arizona, Dept of Physics, Tucson, Arizona 85721.


                               ABSTRACT 

	In a test setup, a hardware neural network determined track parameters 
  of charged particles traversing a drift chamber. Voltages proportional to the 
  drift times in 6 cells of the 3-layer chamber were inputs to the Intel ETANN 
  neural network chip which had been trained to give the slope and intercept of 
  tracks. We compare network track parameters to those obtained from off-line 
  track fits. To our knowledge this is the first on-line application of a VLSI 
  neural network to a high energy physics detector. This test explored the 
  potential of the chip and the practical problems of using it in a real world 
  setting. We compare chip performance to a neural network simulation on a 
  conventional computer. We discuss possible applications of the chip in high 
  energy physics detector triggers.

	    Accepted by Nuclear Instruments and Methods, Section A

 * FERMILAB-Pub-92/55