Structured sparsity guided training in an artificial neural network
US12430543B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 21, 2020 |
| Grant date | Sep 30, 2025 |
| Priority date | — |
| Expiry date | Aug 9, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06V10/955
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A novel and useful system and method of improved power performance and lowered memory requirements for an artificial neural network based on packing memory utilizing several structured sparsity mechanisms. The invention applies to neural network (NN) processing engines adapted to implement mechanisms to search for structured sparsity in weights and activations, resulting in a considerably reduced memory usage. The sparsity guided training mechanism synthesizes and generates structured sparsity weights. A compiler mechanism within a software development kit (SDK), manipulates structured weight domain sparsity to generate a sparse set of static weights for the NN. The structured sparsity static weights are loaded into the NN after compilation and utilized by both the structured weight domain sparsity mechanism and the structured activation domain sparsity mechanism. The application of structured sparsity lowers the span of search options and creates a relatively loose coupling between the data and control planes.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.