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Artificial neural network implementation on a fine-grained FPGA

1994; Springer Science+Business Media; Linguagem: Inglês

10.1007/3-540-58419-6_126

1611-3349

Patrick Lysaght, J. Stockwood, Jason Law, D. Girma,

Neural Networks and Reservoir Computing

This paper reports on the implementation of an Artificial Neural Network (ANN) on an Atmel AT6005 Field Programmable Gate Array (FPGA). The work was carried out as an experiment in mapping a bit-level, logically intensive application onto the specific logic resources of a fine-grained FPGA. By exploiting the reconfiguration capabilities of the Atmel FPGA, individual layers of the network are time multiplexed onto the logic array. This allows a larger ANN to be implemented on a single FPGA at the expense of slower overall system operation.