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Project ID: IST-2001-34712
Finanziato nell'ambito di: FP5-IST
Paese: Germany

A mixed-signal neural network ASIC implementing binary neurons and analog synapses

Inside each neural network ASIC, four analog Perceptron-based networks process the information. Their communication with each other as well as with other parts of the system is entirely digital. Each network block can implement a fully connected recursive network with 128 input and 64 output neurons. The weight values are stored on capacitances inside the synapses.

A specialized weight storage circuit is able to load up to 400 million weight values per second. The neuron operation is based on the summation of currents generated in the synapses. If the excitatory exceeds the inhibitory current, the synapse will fire. This differential neuron input leads to high noise immunity and fast operation.

A single network block, containing more than 8000 synapses, uses only 1.5 mm{2} silicon area. The high speed interface is realized by bidirectional low-voltage differential signalling (LVDS). This allows a high throughput without the generation of digital switching noise. 16 integrated digital-to-analog converters translate the numerical weight values into the according strengths of the synaptic connections.

Reported by

RUPRECHT-KARLS-UNIVERSITAET HEIDELBERG
Im Neuenheimer Feld 227
69120 HEIDELBERG
Germany
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