FP16 (Half Precision) and BF16 (BFloat16) are two different floating-point formats used in computing, particularly in hardware architectures and deep learning frameworks. They are designed to strike a balance between computational efficiency and numerical range, making them suitable for tasks like neural network training and inference. Here's a comparison of FP16 and BF16 with examples:
FP16 (Half Precision): In FP16, a floating-point number is represented using 16 bits. It consists of 1 sign bit, 5 bits for the exponent, and 10 bits for the fraction (mantissa). This limited precision allows a wide range of numbers to be represented, but it sacrifices precision for very small or large values.
BF16 (BFloat16): BF16 uses 16 bits as well, but with a different distribution. It has 1 sign bit, 8 bits for the exponent, and 7 bits for the mantissa. This format is designed to retain more precision for small values while still accommodating a broad range of numbers.
Both FP16 and BF16 can represent a wide range of numbers, including very small and very large values, thanks to their exponent components.
Let's use an example to illustrate the differences between FP16 and BF16:
Imagine we have a very large neural network with many layers, and we're training it using gradient descent. During each iteration, the weights of the network are updated using the gradients. Here, we'll consider a simplified scenario with just one weight parameter and its gradient.
FP16 (Half Precision):
Weight in FP16: 0.001 becomes approximately 0.00098 due to precision loss.
Gradient in FP16: 0.00001 remains unchanged.
When the weight is updated using the gradient, the limited precision of FP16 can lead to significant precision loss in the weight, affecting the overall convergence of the neural network.
Weight in BF16: 0.001 remains almost unchanged.
Gradient in BF16: 0.00001 remains almost unchanged.
In this case, BF16 retains more precision for both the weight and gradient, which can lead to better convergence during training.
FP16 is commonly used in deep learning training and inference due to its ability to accelerate computations by performing more calculations per unit time.
BF16 is becoming more popular in hardware architectures designed for machine learning tasks. It's particularly useful when preserving gradient information during training is crucial for convergence.
In summary, while both FP16 and BF16 offer benefits for certain applications, BF16 is designed to strike a better balance between precision and range, making it well-suited for deep learning tasks where numerical stability and convergence are essential.