Simplicial Piecewise Linear Computation Complexity for Vector-Vector Products

Abstract

In this paper we analyze several simplicial piecewise-linear digital computation architectures that are good candidates to replace the multiply and accumulate (MAC) units in deep neural networks. We analyze them in terms of area and energy of a digital VLSI realization and compare the results with the classic MAC architectures.