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 VME board for trigger and on- and off-line analysis.
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