Artificial neural network training using flexible floating point tensors
US12205035B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 8, 2018 |
| Grant date | Jan 21, 2025 |
| Priority date | — |
| Expiry date | Feb 12, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2207/382
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Thus, the present disclosure is directed to systems and methods for training neural networks using a tensor that includes a plurality of FP16 values and a plurality of bits that define an exponent shared by some or all of the FP16 values included in the tensor. The FP16 values may include IEEE 754 format 16-bit floating point values and the tensor may include a plurality of bits defining the shared exponent. The tensor may include a shared exponent and FP16 values that include a variable bit-length mantissa and a variable bit-length exponent that may be dynamically set by processor circuitry. The tensor may include a shared exponent and FP16 values that include a variable bit-length mantissa; a variable bit-length exponent that may be dynamically set by processor circuitry; and a shared exponent switch set by the processor circuitry to selectively combine the FP16 value exponent with the shared exponent.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.