VLSI for Artificial Intelligence and Neural Networks 1991
DOI: 10.1007/978-1-4615-3752-6_32
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A VLSI Implementation of a Generic Systolic Synaptic Building Block for Neural Networks

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Cited by 16 publications
(2 citation statements)
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“…A second class of digital implementations considers simulation of the neural network by distributing the computation in the whole array instead of associating neurons to array nodes [29]. No mapping (possibly through time multiplexing) of synapses onto interconnection links can be identified: a row of the rectangular array is dedicated to simulation of an individual neuron (see Fig.…”
Section: Mapping Of Faults Onto Behavioral Errors In Vlsi Implementatmentioning
confidence: 99%
“…A second class of digital implementations considers simulation of the neural network by distributing the computation in the whole array instead of associating neurons to array nodes [29]. No mapping (possibly through time multiplexing) of synapses onto interconnection links can be identified: a row of the rectangular array is dedicated to simulation of an individual neuron (see Fig.…”
Section: Mapping Of Faults Onto Behavioral Errors In Vlsi Implementatmentioning
confidence: 99%
“…is dependent on the function itself. In the general case, a self-dual circuit can be designed as shown in [15]. This may require a circuit whose complexity is more than twice the nominal one since one of the circuits implements the nominal function, another implements the dual function, and a multiplexer selects the output.…”
Section: Alternating Logicmentioning
confidence: 99%