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- W2038030359 abstract "Abstract In this paper we propose no learning based neural networks for serial binary multiplication. We show that for “subarray-wise” generation of the partial product matrix and a data transmission rate of δ bits per cycle the serial multiplication of two n bits operands can be computed in ⌈n/δ⌉ serial cycles with an O (nδ) size neural network, and maximum fan-in and weight values both in the order of O (δ log δ) . The minimum delay for this scheme is in the order of ⌈ n ⌉+ log n and it corresponds to a data transmission rate of ⌈ n ⌉ bits per cycle. For “column-wise” generation of the partial product matrix and a data transmission rate of 1-bit per cycle the serial multiplication can be achieved in 2n−1+(k+1)⌈ log k n⌉ delay with a (k+1)(n−1)/(k−1) size neural network, a maximum weight of 2 k and a maximum fan-in of 3k+1 . If a data transmission rate of δ bits per serial cycle is assumed we prove a delay of ⌈(2n−1)/δ⌉+(δ+1)⌈ log n⌉ for a (δ+1)(n−1) size neural network, a maximum weight of 2 δ and a maximum fan-in of 3δ+1 ." @default.
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- W2038030359 date "1999-10-01" @default.
- W2038030359 modified "2023-09-26" @default.
- W2038030359 title "Serial binary multiplication with feed-forward neural networks" @default.
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- W2038030359 doi "https://doi.org/10.1016/s0925-2312(98)00112-x" @default.
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