High precision FPGA implementation of neural network activation functions

The efficient implementation of artificial neural networks in FPGA boards requires tackling several issues that strongly affect the final result. One of these issues is the computation of the neuron's activation function. In this work, a detailed analysis of the FPGA implementations of the Sigmoid and Exponential functions is carried out, in a approach combining a lookup table with a linear interpolation procedure. Further, to optimize board resources utilization, a time division multiplexing of the multiplier attached to the neurons was used. The results are evaluated in terms of the absolute and relative errors obtained and also through measuring a quality factor and the resource utilization, showing a clear improvement in relationship to previously published works.