  In this paper, we present several resource-efficient algorithmic solutions
regarding the fully parallel hardware implementation of the basic filtering
operation performed in the convolutional layers of convolution neural networks.
In fact, these basic operations calculate two inner products of neighboring
vectors formed by a sliding time window from the current data stream with an
impulse response of the M-tap finite impulse response filter. We used Winograd
minimal filtering trick and applied it to develop fully parallel
hardware-oriented algorithms for implementing the basic filtering operation for
M=3,5,7,9, and 11. A fully parallel hardware implementation of the proposed
algorithms in each case gives approximately 30 percent savings in the number of
embedded multipliers compared to a fully parallel hardware implementation of
the naive calculation methods.
