Nonlinear Operation

Nonlinear operations are fundamental to many machine learning models, particularly deep neural networks, but their computational cost can be substantial. Current research focuses on optimizing these operations through techniques like piecewise linear approximation, look-up tables (LUTs), and the use of simpler neural networks as approximators for complex nonlinearities within architectures such as transformers and recurrent neural networks. These efforts aim to improve efficiency, reduce hardware requirements (e.g., power consumption and area), and maintain accuracy, impacting areas like neural network verification and hardware acceleration.