Abstract
The neural chip SAND (Simple Applicable Neural Device) was designed to accelerate computations of neural networks at a very low cost basis, due to the fact that only few peripheral chips are necessary to use the neural network chip in applications. Four SAND-chips were implemented on one PCI-board. The board is highly usable for hardware triggers in particle physics. The performance of a SAND-PCI-board is 800 Mega Connections per Second due to four neuro-chips, each with four parallel 16 bit multipliers and 40 bit adders. SAND 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. The application of the SAND-PCI-board is proposed for cosmic ray physics to allow online analysis of extensive air showers.
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