Title :
High speed neural network chip for trigger purposes in high energy physics
Author :
Eppler, W. ; Fischer, T. ; Gemmeke, H. ; Menchikov, A.
Author_Institution :
Forschungszentrum Karlsruhe, Germany
Abstract :
A novel neural chip SAND (Simple Applicable Neural Device) is described. It is highly usable for hardware triggers in particle physics. The chip is optimized for a high input data rate (50 MHz, 16 bit data) at a very low cost basis. The performance of a single SAND chip is 200 MOPS due to four parallel 16 bit multipliers and 40 bit adders working in one clock cycle. The chip is able to implement feedforward neural networks with a maximum of 512 input neurons and three hidden layers. Kohonen feature maps and radial basis function networks may be also calculated. Four chips will be implemented on a PCI-board for simulation and on a VWE board for trigger and on- and off-line analysis
Keywords :
feedforward neural nets; neural chips; nuclear electronics; self-organising feature maps; trigger circuits; 16 bit; 40 bit; 50 MHz; Kohonen feature map; PCI board; SAND; Simple Applicable Neural Device; VWE board; feedforward neural network; hardware trigger; high energy particle physics; high speed neural network chip; radial basis function network; simulation; Artificial neural networks; Clocks; Cost function; Data analysis; Hip; Intelligent networks; Neural networks; Neurons; Pattern recognition; Physics;
Conference_Titel :
Design, Automation and Test in Europe, 1998., Proceedings
Conference_Location :
Paris
Print_ISBN :
0-8186-8359-7
DOI :
10.1109/DATE.1998.655844
