  This paper presents a structural design of the hardware-efficient module for
implementation of convolution neural network (CNN) basic operation with reduced
implementation complexity. For this purpose we utilize some modification of the
Winograd minimal filtering method as well as computation vectorization
principles. This module calculate inner products of two consecutive segments of
the original data sequence, formed by a sliding window of length 3, with the
elements of a filter impulse response. The fully parallel structure of the
module for calculating these two inner products, based on the implementation of
a naive method of calculation, requires 6 binary multipliers and 4 binary
adders. The use of the Winograd minimal filtering method allows to construct a
module structure that requires only 4 binary multipliers and 8 binary adders.
Since a high-performance convolutional neural network can contain tens or even
hundreds of such modules, such a reduction can have a significant effect.
