BF16

BF16 refers to BFloat16, a 16-bit floating-point format. It is a compromise between 32-bit floating-point (FP32) and 16-bit floating-point (FP16). BF16 has the same exponent range as FP32, which allows it to represent a wider range of numbers, but it has a shorter significand, meaning it has lower precision. This format is particularly useful in machine learning and deep learning applications where the wider exponent range of BF16 can prevent underflow or overflow issues during training, while the reduced precision is often acceptable for these tasks.

The adoption of BF16 has been driven by the need for more efficient computation and memory usage in deep learning. By using 16-bit numbers instead of 32-bit, hardware can perform computations faster and consume less memory. This leads to faster training times and the ability to work with larger models or larger batch sizes on existing hardware. Several modern hardware accelerators, including GPUs and TPUs, have added support for BF16 operations.

BF16 differs from the more common FP16 format in its structure. While both are 16-bit, FP16 follows the IEEE 754 standard with a smaller exponent range and a larger significand compared to BF16. This means FP16 offers higher precision for numbers within its range but is more susceptible to overflow and underflow. BF16's design prioritizes the ability to represent very large and very small numbers accurately, which is beneficial for the cumulative effect of operations in neural network training.