Floating Point

Floating-point representation, crucial for numerical computation, is undergoing significant refinement to improve efficiency and accuracy in resource-constrained environments like embedded systems and large language models. Current research focuses on optimizing precision through techniques like block floating-point, mixed-precision training (combining different floating-point formats), and novel quantization methods (e.g., ternary, integer, and low-bit floating-point representations) applied to various architectures including convolutional neural networks and transformers. These advancements are vital for deploying computationally intensive applications like deep learning on power-limited devices and for accelerating the training and inference of large models, impacting both scientific computing and practical applications.