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QPA: A Quantization-Aware Piecewise Polynomial Approximation Methodology for Hardware-Efficient Implementations
<p>Piecewise polynomial approximation on non-linear functions plays an important role in neural network accelerators and digital signal processing. In this paper, we proposed QPA, a quantization-aware piecewise polynomial approximation methodology, to generate the optimized coefficients for hardware implementations targeting any polynomial order. QPA incorporated several key features to minimize the fitting error and the hardware cost, including using the Remez algorithm to compute the min-maxdoi:10.36227/techrxiv.21532914.v1 fatcat:dd7lzgty2vgxvel56fxevzrh6u