Integer Only Training

Integer-only training aims to replace floating-point arithmetic in deep learning with integer operations, reducing computational cost and memory requirements for training and inference. Current research focuses on adapting various neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, to this integer-only paradigm, often employing novel activation functions and training algorithms like direct feedback alignment to mitigate challenges like integer overflow. This approach holds significant promise for deploying deep learning models on resource-constrained devices and improving energy efficiency, impacting both scientific research and practical applications in areas like edge computing and mobile AI.