Event-based classification of features in a reconfigurable and temporally coded convolutional spiking neural network
US11227210B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 24, 2020 |
| Grant date | Jan 18, 2022 |
| Priority date | — |
| Expiry date | Jul 24, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG11C11/54
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments of the present invention provides a system and method of learning and classifying features to identify objects in images using a temporally coded deep spiking neural network, a classifying method by using a reconfigurable spiking neural network device or software comprising configuration logic, a plurality of reconfigurable spiking neurons and a second plurality of synapses. The spiking neural network device or software further comprises a plurality of user-selectable convolution and pooling engines. Each fully connected and convolution engine is capable of learning features, thus producing a plurality of feature map layers corresponding to a plurality of regions respectively, each of the convolution engines being used for obtaining a response of a neuron in the corresponding region. The neurons are modeled as Integrate and Fire neurons with a non-linear time constant, forming individual integrating threshold units with a spike output, eliminating the need for multiplication and addition of floating-point numbers.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.