ADDITION USING CONSTRAINED THRESHOLD GATES 1 or ❝ When Neural Networks Go VLSI

@inproceedings{Beiu1994ADDITIONUC,
  title={ADDITION USING CONSTRAINED THRESHOLD GATES 1 or ❝ When Neural Networks Go VLSI},
  author={Valeriu Beiu and Jan A. Peperstraete and Joos Vandewalle and Rudy Lauwereins},
  year={1994}
}
In this paper we show that efficient VLSI implementations of ADDITION are possible using constrained threshold gates (i.e. having limited fan-in and range of weights). We introduce a class of Boolean functions F▲ and while proving that ∀ f∆ ∈ F▲ is linearly separable, we discover that each f∆ function can be built starting from the previous one (f∆−2) by copying its synaptic weights. As the G-functions computing the carry bits are a subclass of F▲, we are able to build a set of “neural networks… CONTINUE READING