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Comparing bfloat16 Range and Precision to Other 16-bit Numbers

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Comparing bfloat16 Range and Precision to Other 16-bit Numbers

Deep Learning has shown the way to new floating point formats. We take a look at bfloat16 and what it means for computing.

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Deep Learning has spurred interest in novel floating point formats. Algorithms often don't need as much precision as standard IEEE-754 doubles or even single precision floats. Lower precision makes it possible to hold more numbers in memory, reducing the time spent swapping numbers in and out of memory. Since this where a lot of time goes, low precision formats can speed things up quite a bit.

Here I want to look at bfloat16, or BF16 for short, and compare it to 16-bit number formats I've written about previously, IEEE and posit.

Bit Layout

The BF16 format is sort of a cross between FP16 and FP32, the 16- and 32-bit formats defined in the IEEE 754-2008 standard, also known as half precision and single precision.

The bfloat16 format has 16 bits like FP16, but has the same number of exponent bits as FP32. Each number has 1 sign bit. The rest of the bits in each of the formats are allocated in the table below.

    | Format | Bits | Exponent | Fraction |
    | FP32   |   32 |        8 |       23 |
    | FP16   |   16 |        5 |       10 |
    | BF16   |   16 |        8 |        7 |

BF16 has as many bits as a FP16, but as many exponent bits as a FP32. The latter makes conversion between BF16 and FP32 easy. Chop off the last 16 bits off a FP32 and you have a BF16, or pad a BF16 with zeros to make a FP32.


The epsilon value, the smallest number ε such that 1 + ε > 1 in machine representation, is 2 where e is the number of exponent bits. BF16 has much less precision near 1 than the other formats.

    | Format |    Epsilon |
    | FP32   | 0.00000012 |
    | FP16   | 0.00390625 |
    | BF16   | 0.03125000 |

Dynamic range

The dynamic range of bfloat16 is similar to that of a IEEE single precision number. Relative to FP32, BF16 sacrifices precision to retain range. Range is mostly determined by the number of exponent bits, though not entirely.

Dynamic range in decades is the log base 10 of the ratio of the largest to smallest representable positive numbers. The dynamic ranges of the numeric formats are given below. (Python code to calculate dynamic range is given here.)

    | Format | DR    |
    | FP32   | 83.38 |
    | BF16   | 78.57 |
    | FP16   | 12.04 |

Comparison to Posits

The precision and dynamic range of posit numbers depends on how many bits you allocate to the maximum exponent, denoted es by convention. (Note "maximum." The number of exponent bits varies for different numbers.) This post explains the anatomy of a posit number.

Posit numbers can achieve more precision and more dynamic range than IEEE-like floating point numbers with the same number of bits. Of course, there's no free lunch. Posits represent large numbers with low precision and small numbers with high precision, but this trade-off is often what you'd want.

For an n-bit posit, the number of fraction bits near 1 is n - 2 - es and so epsilon is 2 to the exponent es - n - 2. The dynamic range is...

...which is derived here. The dynamic range and epsilon values for 16-bit posits with es ranging from 1 to 4 are given in the table below.

    | es |     DR |   epsilon |
    |  1 |  16.86 | 0.0000076 |
    |  2 |  33.82 | 0.0000153 |
    |  3 |  37.43 | 0.0000305 |
    |  4 | 143.86 | 0.0000610 |

For all the values of es above, a 16-bit posit number has a smaller epsilon than either FP16 or BF16. The dynamic range of a 16-bit posit is larger than that of a FP16 for all values of es, and greater than BF16 and FP32 when es = 4.

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math ,floating point ,numbers ,ai ,deep learning ,precision ,range

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