Systems and methods for quantizing neural networks via periodic regularization functions
US11468313B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 12, 2018 |
| Grant date | Oct 11, 2022 |
| Priority date | — |
| Expiry date | Aug 12, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/063
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The disclosed computer-implemented method may include (1) identifying an artificial neural network comprising a set of nodes interconnected via a set of connections, and (2) training the artificial neural network by, for each connection in the set of connections, determining a quantized weight value associated with the connection. Determining the quantized weight value associated with the connection may include (1) associating a loss function with the connection, the loss function including a periodic regularization function that describes a relationship between an input value and a weight value of the connection, (2) determining a minimum of the associated loss function with respect to the weight value in accordance with the periodic regularization function, and (3) generating the quantized weight value associated with the connection based on the determined minimum of the loss function. Various other methods, systems, and computer-readable media are also disclosed.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.