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This paper describes an optimized implementation of a Forward PropagatingClassification Neural Network which has been previously trained. Theimplementation described highlights a novel means of using Python scripts togenerate a Verilog hardware implementation. The characteristics of thisimplementation include optimizations to scale input data, use selected addendsinstead of multiplication functions, hardware friendly activation functions andsimplified output selection. Inference performance comparison of a 28x28 pixel'hand-written' recognition NN between a software implementation on an Intel i7vs a Xilinx FPGA will be detailed.

Optimization Techniques to Improve Inference Performance of a Forward Propagating Neural Network on an FPGA