18621802. OPTICAL NEURAL NETWORK ACCELERATOR simplified abstract (Intel Corporation)
Contents
OPTICAL NEURAL NETWORK ACCELERATOR
Organization Name
Inventor(s)
Songtao Liu of Santa Clara CA (US)
Haisheng Rong of Pleasanton CA (US)
Mozhgan Mansuri of Portland OR (US)
Ram Krishnamurthy of Portland OR (US)
OPTICAL NEURAL NETWORK ACCELERATOR - A simplified explanation of the abstract
This abstract first appeared for US patent application 18621802 titled 'OPTICAL NEURAL NETWORK ACCELERATOR
The patent application describes technology that performs matrix-matrix multiplication operations in an optical neural network (ONN) using a first plurality of panels to generate output optical signals based on input optical signals and weights.
- The technology includes an input panel, a weight panel, and a photodetector panel within the first plurality of panels.
- The input panel generates input optical signals representing the input to the matrix-matrix multiplication operation.
- The weight panel represents the weights of the first layer of the ONN.
- The photodetector panel generates output photodetector signals based on the output optical signals generated from the input signals and weights.
Potential Applications: - Optical computing - Artificial intelligence - Machine learning
Problems Solved: - Efficient matrix-matrix multiplication in optical neural networks - Enhanced performance of neural network operations
Benefits: - Faster processing speeds - Reduced energy consumption - Improved accuracy in computations
Commercial Applications: Optical neural networks can be utilized in various industries such as: - Healthcare for medical imaging analysis - Finance for fraud detection - Autonomous vehicles for real-time decision making
Prior Art: Researchers can explore prior studies on optical neural networks, matrix-matrix multiplication in neural networks, and photodetector technology.
Frequently Updated Research: Stay updated on advancements in optical computing, neural network architectures, and photonic technologies relevant to this innovation.
Questions about Optical Neural Networks: 1. How does the use of optical signals in neural networks differ from traditional electronic signals? 2. What are the key challenges in scaling up optical neural networks for practical applications?
Original Abstract Submitted
Systems, apparatuses and methods include technology that executes, with a first plurality of panels, a first matrix-matrix multiplication operation of a first layer of an optical neural network (ONN) to generate output optical signals based on input optical signals that pass through an optical path of the ONN, and weights of the first layer of the ONN. The first plurality of panels includes an input panel, a weight panel and a photodetector panel. The executing includes generating, with the input panel, the input optical signals, where the input optical signals represent an input to the first matrix-matrix multiplication operation of the first layer of the ONN, representing, with the weight panel, the weights of the first layer of the ONN, and generating, with the photodetector panel, output photodetector signals based on the output optical signals that are generated based on the input optical signals and the weights.
