DIF-LUT Pro: An Automated Tool for Simple yet Scalable Approximation of Nonlinear Activation on FPGA

Nonlinear activation plays an essential role in neural networks (NNs) for their generalization ability. However, implementing intricate mathematical operations on hardware platforms, including Field-Programmable Gate Arrays (FPGAs), presents significant challenges. Prior works based on piecewise functions or look-up table (LUT) have encountered difficulties in balancing precision requirements with fair hardware overhead and often necessitating complex manual interventions. To address these issues, this paper proposes DIF-LUT Pro, an automated tool for simple yet scalable approximation for various nonlinear activations on FPGA. Specifically, the proposed algorithm achieves self-adaptive hardware design oriented towards target precision, by piecewise linear matching to fit the function derivative roughly and range addressable LUT to offset the difference. Moreover, DIF-LUT Pro integrates the algorithm into an automated tool, allowing users to configure the customized interface and generate the corresponding hardware description language (HDL) code with a single click. Experimental results show that (1) DIF-LUT Pro features robust automation and fair generality, capable of generating equitable hardware designs under various user configurations across different FPGA platforms; (2) DIF-LUT Pro produces approximations that are simple yet effective, achieving competitive performance compared to previous expert-crafted designs. Furthermore, two detailed case studies demonstrate the efficient application of DIF-LUT Pro on NeRF and SEResnet, proving its practical value. Our source code is open-source and available at https://github.com/AdrianLiu00/DIF-LUT-Tool.