Hardware-aware Softmax Approximation for Deep Neural Networks

There has been a rapid development of custom hardware for accelerating the inference speed of deep neural networks (DNNs), by explicitly incorporating hardware metrics (e.g., area and energy) as additional constraints, in addition to application accuracy. Recent efforts mainly focused on linear functions (matrix multiplication) in convolutional (Conv) or fully connected (FC) layers, while there is no publicly available study on optimizing the inference of non-linear functions in DNNs, with hardware constraints.