Semiprecision

Semiprecision refers to a range of floating-point formats that offer more precision than standard single-precision (FP32) but less than double-precision (FP64). These formats are often used in machine learning and deep learning applications to reduce memory usage and computational cost without a significant loss of accuracy for many tasks.

Common semiprecision formats include half-precision (FP16) and bfloat16. FP16 uses 16 bits, with 1 sign bit, 5 exponent bits, and 10 mantissa bits. This format offers a wide dynamic range but can suffer from underflow or overflow issues. Bfloat16 also uses 16 bits but allocates 8 bits for the exponent and 7 bits for the mantissa. This distribution allows for a dynamic range similar to FP32, making it more robust for certain numerical operations.

The adoption of semiprecision formats has been driven by the increasing demand for faster training and inference of large neural networks. Graphics processing units (GPUs) and specialized AI accelerators are increasingly designed to perform computations efficiently using these lower-precision formats. This allows for larger models to be trained on available hardware and for real-time inference on edge devices. However, the suitability of semiprecision depends on the specific algorithm and the acceptable error tolerance.