First- and second-order Taylor approximations to softmax deliver up to 14% FPGA resource reduction with at most 0.2% accuracy loss on LeNet-5 and MobileNet v2.
Cordic-based Softmax Acceleration Method of Convolu- tion Neural Network on FPGA,
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A Quantitative Evaluation of Approximate Softmax Functions for Deep Neural Networks
First- and second-order Taylor approximations to softmax deliver up to 14% FPGA resource reduction with at most 0.2% accuracy loss on LeNet-5 and MobileNet v2.