Low Weight and Fan-In Neural Networks for Basic Arithmetic Operations
نویسندگان
چکیده
In this paper we investigate low weight and fan-in neural networks for the precise computation of some basic arithmetic operations. First we assume one bit per serial cycle LSB first operand reception and introduce a pipeline network performing serial binary addition in O(n) time constructed with 11 threshold gates, a maximum weight of 2 and a maximum fan-in of 4. Further we prove that serial multiplication can be implemented with a threshold network constructed with 11(n 1) threshold gates and the same maximal values for fan-in and weights. The achieved delay performance is in the order of 2n 1 + 2dlogne. Consequently we propose schemes for the addition of 32-bit operands based on a “carry look ahead” approach. In particular we show that the 32-bit 2 1 addition can be implemented in depth-8=7=5, with a maximum fan-in of 4=4=6 and a maximum weight of 2=4=5, respectively. We finally show that the 2 1 binary addition using redundant represented operands can be performed by a depth-3 threshold networks with 12n size, a maximum fan-in of 5 and a maximum weight of 2.
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