Bfloat16 floating-point format

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The bfloat16 (Brain Floating Point) [1] [2] floating-point format is a computer number format occupying 16 bits in computer memory; it represents a wide dynamic range of numeric values by using a floating radix point. This format is a truncated (16-bit) version of the 32-bit IEEE 754 single-precision floating-point format (binary32) with the intent of accelerating machine learning and near-sensor computing. [3] It preserves the approximate dynamic range of 32-bit floating-point numbers by retaining 8 exponent bits, but supports only an 8-bit precision rather than the 24-bit significand of the binary32 format. More so than single-precision 32-bit floating-point numbers, bfloat16 numbers are unsuitable for integer calculations, but this is not their intended use. Bfloat16 is used to reduce the storage requirements and increase the calculation speed of machine learning algorithms. [4]


The bfloat16 format is utilized in Intel AI processors, such as Nervana NNP-L1000, Xeon processors (AVX-512 BF16 extensions), and Intel FPGAs, [5] [6] [7] Google Cloud TPUs, [8] [9] [10] and TensorFlow. [10] [11] ARMv8.6-A, [12] AMD ROCm, [13] and CUDA [14] also support the bfloat16 format. On these platforms, bfloat16 may also be used in mixed-precision arithmetic, where bfloat16 numbers may be operated on and expanded to wider data types.

bfloat16 floating-point format

bfloat16 has the following format:

The bfloat16 format, being a truncated IEEE 754 single-precision 32-bit float, allows for fast conversion to and from an IEEE 754 single-precision 32-bit float; in conversion to the bfloat16 format, the exponent bits are preserved while the significand field can be reduced by truncation (thus corresponding to round toward 0), ignoring the NaN special case. Preserving the exponent bits maintains the 32-bit float's range of ≈ 10−38 to ≈ 3 × 1038. [15]

The bits are laid out as follows:

IEEE half-precision 16-bit float
signexponent (5 bit)fraction (10 bit)
 0  0  1  1  0  0  0  1  0  0  0  0  0  0  0  0 
IEEE 754 single-precision 32-bit float
signexponent (8 bit)fraction (23 bit)
 0  0  1  1  1  1  1  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 
signexponent (8 bit)fraction (7 bit)
 0  0  1  1  1  1  1  0  0  0  1  0  0  0  0  0 
NVidia's TensorFloat
signexponent (8 bit)fraction (10 bit)
 0  0  1  1  1  1  1  0  0  0  1  0  0  0  0  0  0  0  0 
AMD's fp24 format
signexponent (7 bit)fraction (16 bit)
 0  0  1  1  1  1  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0 
Pixar's PXR24 format
signexponent (8 bit)fraction (15 bit)
 0  0  1  1  1  1  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0 

Contrast with bfloat16 and single precision



  •   S: sign
  •   E: exponent
  •   F: fraction (trailing significand) in both formats
  •   f: fraction (trailing significand) in single precision only

Exponent encoding

The bfloat16 binary floating-point exponent is encoded using an offset-binary representation, with the zero offset being 127; also known as exponent bias in the IEEE 754 standard.

Thus, in order to get the true exponent as defined by the offset-binary representation, the offset of 127 has to be subtracted from the value of the exponent field.

The minimum and maximum values of the exponent field (00H and FFH) are interpreted specially, like in the IEEE 754 standard formats.

ExponentSignificand zeroSignificand non-zeroEquation
00H zero, −0 subnormal numbers (−1)signbit×2−126× 0.significandbits
01H, ..., FEHnormalized value(−1)signbit×2exponentbits−127× 1.significandbits
FFH±infinity NaN (quiet, signaling)

The minimum positive normal value is 2−126 ≈ 1.18 × 10−38 and the minimum positive (subnormal) value is 2−126−7 = 2−133 ≈ 9.2 × 10−41.

Encoding of special values

Positive and negative infinity

Just as in IEEE 754, positive and negative infinity are represented with their corresponding sign bits, all 8 exponent bits set (FFhex) and all significand bits zero. Explicitly,

val    s_exponent_signcnd +inf = 0_11111111_0000000 -inf = 1_11111111_0000000 

Not a Number

Just as in IEEE 754, NaN values are represented with either sign bit, all 8 exponent bits set (FFhex) and not all significand bits zero. Explicitly,

val    s_exponent_signcnd +NaN = 0_11111111_klmnopq -NaN = 1_11111111_klmonpq 

where at least one of k, l, m, n, o, p, or q is 1. As with IEEE 754, NaN values can be quiet or signaling, although there are no known uses of signaling bfloat16 NaNs as of September 2018.

Range and precision

Bfloat16 is designed to maintain the number range from the 32-bit IEEE 754 single-precision floating-point format (binary32), while reducing the precision from 24 bits to 8 bits. This means that the precision is between two and three decimal digits, and bfloat16 can represent finite values up to about 3.4 × 1038.


These examples are given in bit representation, in hexadecimal and binary, of the floating-point value. This includes the sign, (biased) exponent, and significand.

3f80 = 0 01111111 0000000 = 1 c000 = 1 10000000 0000000 = −2
7f7f = 0 11111110 1111111 = (28 − 1) × 2−7 × 2127 ≈ 3.38953139 × 1038 (max finite positive value in bfloat16 precision) 0080 = 0 00000001 0000000 = 2−126 ≈ 1.175494351 × 10−38 (min normalized positive value in bfloat16 precision and single-precision floating point)

The maximum positive finite value of a normal bfloat16 number is 3.38953139 × 1038, slightly below (224 − 1) × 2−23 × 2127 = 3.402823466 × 1038, the max finite positive value representable in single precision.

Zeros and infinities

0000 = 0 00000000 0000000 = 0 8000 = 1 00000000 0000000 = −0
7f80 = 0 11111111 0000000 = infinity ff80 = 1 11111111 0000000 = −infinity

Special values

4049 = 0 10000000 1001001 = 3.140625 ≈ π ( pi ) 3eab = 0 01111101 0101011 = 0.333984375 ≈ 1/3


ffc1 = x 11111111 1000001 => qNaN ff81 = x 11111111 0000001 => sNaN

See also

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  2. Wang, Shibo; Kanwar, Pankaj (2019-08-23). "BFloat16: The secret to high performance on Cloud TPUs". Google Cloud. Retrieved 2020-08-11. This custom floating point format is called “Brain Floating Point Format,” or “bfloat16” for short. The name flows from “Google Brain”, which is an artificial intelligence research group at Google where the idea for this format was conceived.
  3. Tagliavini, Giuseppe; Mach, Stefan; Rossi, Davide; Marongiu, Andrea; Benin, Luca (2018). "A transprecision floating-point platform for ultra-low power computing". 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE). pp. 1051–1056. arXiv: 1711.10374 . doi:10.23919/DATE.2018.8342167. ISBN   978-3-9819263-0-9. S2CID   5067903.
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  5. Khari Johnson (2018-05-23). "Intel unveils Nervana Neural Net L-1000 for accelerated AI training". VentureBeat. Retrieved 2018-05-23. ...Intel will be extending bfloat16 support across our AI product lines, including Intel Xeon processors and Intel FPGAs.
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  8. "Available TensorFlow Ops | Cloud TPU | Google Cloud". Google Cloud. Retrieved 2018-05-23. This page lists the TensorFlow Python APIs and graph operators available on Cloud TPU.
  9. Elmar Haußmann (2018-04-26). "Comparing Google's TPUv2 against Nvidia's V100 on ResNet-50". RiseML Blog. Archived from the original on 2018-04-26. Retrieved 2018-05-23. For the Cloud TPU, Google recommended we use the bfloat16 implementation from the official TPU repository with TensorFlow 1.7.0. Both the TPU and GPU implementations make use of mixed-precision computation on the respective architecture and store most tensors with half-precision.
  10. 1 2 Tensorflow Authors (2018-07-23). "ResNet-50 using BFloat16 on TPU". Google. Retrieved 2018-11-06.
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  15. "Livestream Day 1: Stage 8 (Google I/O '18) - YouTube". Google. 2018-05-08. Retrieved 2018-05-23. In many models this is a drop-in replacement for float-32