Low Precision

Low precision computing aims to improve the efficiency of machine learning and other computationally intensive tasks by reducing the number of bits used to represent numbers, thereby decreasing memory usage, energy consumption, and computation time. Current research focuses on developing novel algorithms and hardware architectures to mitigate the accuracy loss inherent in low-precision computations, including techniques like mixed-precision strategies, optimized matrix multiplication methods, and the use of alternative number systems (e.g., Posits). These advancements are crucial for deploying large language models and other complex AI applications on resource-constrained devices, such as mobile phones and embedded systems, and for accelerating high-performance computing in general.