O(n) depth-3 binary addition

We investigate small depth and size feed-forward neural networks performing binary addition. We propose a set of equations that can be used to realize small depth inexpensive networks for arbitrary operand lengths. In particular we show that O(n) depth-3 networks for the binary addition can be easily constructed having small weight sizes. We also describe a scheme for the design of 32-bit binary adders. When compared to the addition scheme known to produce the least expensive adders on terms of area, using feed-forward neural networks, our scheme requires only 20% of the area in terms of neurons. Consequently our design provides substantial area reduction