  This paper describes an optimized implementation of a Forward Propagating
Classification Neural Network which has been previously trained. The
implementation described highlights a novel means of using Python scripts to
generate a Verilog hardware implementation. The characteristics of this
implementation include optimizations to scale input data, use selected addends
instead of multiplication functions, hardware friendly activation functions and
simplified output selection. Inference performance comparison of a 28x28 pixel
'hand-written' recognition NN between a software implementation on an Intel i7
vs a Xilinx FPGA will be detailed.
