LowPrecision

Low precision refers to numeric representations and computations that use fewer bits than standard high-precision formats, such as 32-bit or 64-bit floating point. The aim is to reduce memory usage, bandwidth, and computational cost while maintaining acceptable accuracy, especially in domains like machine learning, graphics, and embedded systems.

Common forms of low precision include floating-point formats with reduced bit width (for example 16-bit FP16 and BF16, and emerging 8-bit floating-point variants) and fixed-point representations (such as Q-format schemes). Integer formats like int8 or int16 are also widely used in quantized neural networks. The choice of format affects dynamic range, precision, and susceptibility to rounding and saturation.

Low precision introduces quantization errors and potential instability due to restricted dynamic range. To mitigate these issues, techniques such as quantization-aware training, post-training quantization with calibration, mixed precision where sensitive computations use higher precision, and stochastic rounding are employed. These approaches help preserve model accuracy while gaining speed and efficiency. Hardware support from GPUs and dedicated AI accelerators enables fast low-precision arithmetic and efficient memory usage.

Applications extend beyond machine learning to real-time signal processing, computer graphics, and embedded systems where resources are constrained. In ML, low precision often enables faster inference and lower energy consumption, making it attractive for edge devices and large-scale deployments. Ongoing research addresses best practices for precision selection, error bounding, and automatic precision tuning, as well as standardization efforts around low-precision formats in computing.