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Swift introduced half-precision floating point numbers in Swift 5.3 with the Float16 type. [20] OpenCL also supports half-precision floating point numbers with the half datatype on IEEE 754-2008 half-precision storage format. [21] As of 2024, Rust is currently working on adding a new f16 type for IEEE half-precision 16-bit floats. [22]
VCVTPS2PH xmmrm128,ymmreg,imm8 – convert eight single-precision floating point values in a YMM register to half-precision floating-point values in memory or an XMM register. The 8-bit immediate argument to VCVTPS2PH selects the rounding mode. Values 0–4 select nearest, down, up, truncate, and the mode set in MXCSR.RC.
It looks like, from the entry, that 16 bit floating points have a range from 5*10^-8 to 65504 (10^-8 to 10^4), so about 12 orders of magnitude. And, that the precision is about 4 significant figures. As for the section: Precision limitations on integer values, it would probably be more useful to have a chart of epsilon versus number magnitude.
Full Precision" in Direct3D 9.0 is a proprietary 24-bit floating-point format. Microsoft's D3D9 (Shader Model 2.0) graphics API initially supported both FP24 (as in ATI's R300 chip) and FP32 (as in Nvidia's NV30 chip) as "Full Precision", as well as FP16 as "Partial Precision" for vertex and pixel shader calculations performed by the graphics ...
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 × 10 38.
Quadruple-precision floating-point format; Octuple-precision floating-point format; Of these, octuple-precision format is rarely used. The single- and double-precision formats are most widely used and supported on nearly all platforms. The use of half-precision format has been increasing especially in the field of machine learning since many ...
A common usage of mixed-precision arithmetic is for operating on inaccurate numbers with a small width and expanding them to a larger, more accurate representation. For example, two half-precision or bfloat16 (16-bit) floating-point numbers may be multiplied together to result in a more accurate single-precision (32-bit) float. [1]
[50] [51] Some low-precision floating-point formats used for AI acceleration are half-precision and the bfloat16 floating-point format. [ 52 ] [ 53 ] Cerebras Systems has built a dedicated AI accelerator based on the largest processor in the industry, the second-generation Wafer Scale Engine (WSE-2), to support deep learning workloads.