  This paper explores the use of Pyramid Vector Quantization (PVQ) to reduce
the computational cost for a variety of neural networks (NNs) while, at the
same time, compressing the weights that describe them. This is based on the
fact that the dot product between an N dimensional vector of real numbers and
an N dimensional PVQ vector can be calculated with only additions and
subtractions and one multiplication. This is advantageous since tensor
products, commonly used in NNs, can be re-conduced to a dot product or a set of
dot products. Finally, it is stressed that any NN architecture that is based on
an operation that can be re-conduced to a dot product can benefit from the
techniques described here.
