Serialized electro-optic neural network using optical weights encoding
US11373089B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Feb 6, 2019 |
| Grant date | Jun 28, 2022 |
| Priority date | — |
| Expiry date | Mar 16, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/048
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Most artificial neural networks are implemented electronically using graphical processing units to compute products of input signals and predetermined weights. The number of weights scales as the square of the number of neurons in the neural network, causing the power and bandwidth associated with retrieving and distributing the weights in an electronic architecture to scale poorly. Switching from an electronic architecture to an optical architecture for storing and distributing weights alleviates the communications bottleneck and reduces the power per transaction for much better scaling. The weights can be distributed at terabits per second at a power cost of picojoules per bit (versus gigabits per second and femtojoules per bit for electronic architectures). The bandwidth and power advantages are even better when distributing the same weights to many optical neural networks running simultaneously.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.