What is the difference between FP16 and FP32 when doing deep learning?

The terms FP16 and FP32 refer to different numerical formats used in deep learning for representing floating-point numbers.

FP16, also known as half-precision, uses 16 bits to represent a floating-point number. It consists of a 1-bit sign, a 5-bit exponent, and a 10-bit significand (also called mantissa). FP16 provides a smaller range of representable values compared to higher-precision formats but offers faster computations and requires less memory. It is particularly beneficial for applications that prioritize speed and memory efficiency, such as deep learning inference on specialized hardware like graphics processing units (GPUs) or tensor processing units (TPUs).

On the other hand, FP32, also known as single-precision, uses 32 bits to represent a floating-point number. It consists of a 1-bit sign, an 8-bit exponent, and a 23-bit significand. FP32 provides a wider range of representable values and higher precision compared to FP16. It is commonly used in deep learning training tasks where the accuracy of calculations is crucial. FP32 allows for more precise calculations but requires more memory and computational resources compared to FP16.

When it comes to deep learning, the choice between FP16 and FP32 depends on the specific requirements of the task at hand. FP16 can offer significant speedup and memory savings, making it suitable for inference tasks, especially on specialized hardware optimized for lower precision. However, the lower precision of FP16 may lead to a loss of numerical accuracy, which can affect the quality of the results, especially in complex training scenarios. FP32, with its higher precision, is typically preferred for training deep learning models where accuracy is of utmost importance.