Photonic Guided Random Sampling With Noise-Assisted Convergence
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Solution Overview
Problem
Current random data sampling methods in signal processing and machine learning are bottlenecked by the limitations of digital electronic devices, which suffer from low energy efficiency and slow processing speeds. Additionally, photonic computing technology has not been effectively utilized for random data sampling.
Innovation Solution
A method and system for guided random data sampling using a photonic computing system, where input data is converted into an optical signal, guided by a photonic computing module, and then converted back into output data. This system introduces noise as a perturbation to improve convergence and operates significantly faster than digital electronic devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital electronic devices are used for random data sampling, then the processing can be performed reliably, but the computing power and speed become the bottleneck
Solution Approach 1:
The patent replaces digital electronic computing systems with photonic computing systems. The photonic system uses optical signals instead of electrical signals to perform random data sampling operations, thereby achieving significantly higher computing speed while maintaining processing reliability through the physical properties of light propagation and detection.
Solution Approach 2:
The patent changes the fundamental parameter of signal transmission from electrical to optical domain. By using photonic computing, the system achieves faster processing speeds because light travels faster and can be modulated at higher frequencies compared to electrical signals in digital electronic devices.
2Productivity
If photonic computing technology is used for random data sampling, then the energy efficiency and speed improve by several orders of magnitude, but the technology has not been used effectively yet
Solution Approach 1:
The patent divides the photonic computing system into distinct functional modules: a light source for generating optical signals, a modulator for encoding input data onto the optical signals, a photodetector for converting optical signals back to electrical signals, and a processor for performing the random data sampling operations. This modular segmentation simplifies the implementation complexity while maintaining the high speed and energy efficiency benefits of photonic computing.
3Duration of action of moving object
If iterations are performed without perturbation in photonic computing, then the optical waves can be transformed, but the iterations do not converge to a desired solution
Solution Approach 1:
The patent introduces a feedback mechanism where the output of each iteration is fed back as input to the next iteration. The perturbation is applied to the feedback loop, allowing the system to converge to the desired solution by continuously adjusting the optical signals based on the previous iteration's results while maintaining fast transformation speed through photonic computing.
Solution Approach 2:
The patent employs periodic iterations with perturbation applied at each cycle. By repeatedly transforming the optical waves with periodic perturbations, the system achieves convergence to the desired solution while maintaining the high-speed transformation capabilities of photonic computing technology.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The proposed method enables faster and more efficient data processing by leveraging photonic computing, improving convergence by adding perturbations, and overcoming the limitations of digital electronic devices in large-scale computing applications.
Implementation Method 1
modulating an optical wave according to the analog input signal by the modulator to obtain the optical signal
Implementation Method 2
converting the optical signal into a guided optical signal according to guiding data by a photonic computing module
Implementation Method 3
converting the guided optical signal into output data and outputting the output data by a second conversion module
Implementation Method 4
the noise generated by at least one of the first conversion module, the photonic computing module, and the second conversion module is added to the output data as a perturbation
Data Source
AI summary
A method and a system for determining a guided random data sampling are disclosed. The method comprises: converting input data into an optical signal with a variable average intensity through a first conversion module; converting the optical signal into a guided optical signal according to guiding data by a photonic computing module, wherein the average intensity of the guided optical signal varies with the average intensity of the optical signal; and converting the guided optical signal into output data and outputting the output data by a second conversion module; wherein the noise generated by at least one of the first conversion module, the photonic computing module, and the second conversion module is added to the output data as a perturbation. By yielding the perturbation in the optical-analog domain, the output data can be quickly converged to an expected solution in the solving operation of the combinatorial optimization problem.