Photonic Data Processing Array for Parallel CNN Convolution
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Solution Overview
Problem
Convolutional neural networks face computational challenges due to the overwhelming amount of data generated during image processing, which overwhelms computational capacity and requires significant power consumption in traditional digital methods.
Innovation Solution
A photonic neural network system utilizing optical Fourier transforms for convolution operations, enabling faster and more efficient data processing through optical calculations, achieving full-frame parallelism and nearly 100% efficiency by using capacitive light sensors for summing and eliminating noise sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional digital methods are used for convolutional neural network processing, then computational accuracy can be maintained, but processing speed is slow and power consumption is high
Solution Approach 1:
The patent replaces traditional digital electronic computing systems with photonic computing systems that use light instead of electrical signals. Optical Fourier transforms are performed using lenses and optical components, and convolution operations are executed through optical interference and diffraction patterns. This substitution of mechanical/electrical systems with optical systems enables parallel processing of entire images simultaneously, achieving orders of magnitude faster processing speeds while consuming significantly less power since optical systems do not require sequential bit-by-bit computation.
Solution Approach 2:
The patent transitions from sequential digital computation to parallel optical computation by utilizing the spatial dimension of light. Entire images or frames of data are processed simultaneously through optical fields rather than being processed pixel-by-pixel or bit-by-bit in sequence. The optical system uses the wave nature of light to perform Fourier transforms and convolutions across the entire data field in parallel, effectively adding a dimensional aspect to computation that enables massive parallelism and dramatically improves processing speed while reducing power consumption.
2Productivity
If traditional digital methods process large amounts of image data, then complete data analysis is achieved, but computational capacity is overwhelmed
Solution Approach 1:
The patent replaces complex digital computational systems with simpler optical systems that naturally perform Fourier transforms and convolutions through physical optical processes. Instead of requiring powerful digital processors to sequentially compute each operation, the optical system uses lenses, mirrors, and interference patterns to automatically perform these mathematical operations in parallel. This substitution dramatically increases data processing capacity while reducing the complexity of the computational hardware required.
Solution Approach 2:
The optical system performs computations through self-organizing physical processes rather than requiring complex control logic. When light passes through optical components arranged to perform Fourier transforms, the system automatically computes the transforms through wave interference and diffraction. Similarly, convolution operations emerge naturally from optical field interactions. This self-service capability allows the system to process large amounts of data without requiring complex computational architecture, thereby increasing productivity while reducing device complexity.
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 photonic neural network system processes data orders of magnitude faster and with much less power consumption compared to traditional digital methods, supporting all existing convolutional neural net architectures and providing accurate image classification.
Implementation Method 1
A photonic neural network system utilizing optical Fourier transforms for convolution operations
Implementation Method 2
using capacitive light sensors for summing
Data Source
AI summary
A data processing array comprises a plurality of modules, each with a memory, positioned in an array of rows and columns interconnected by a pooling chain that carries data to and receives data from selected ones or groups of the modules. Each modules can also have light modulator elements for transmitting data as light signals and a light sensor for receiving data in the form of modulated light. Pooling switches in the pooling chain between modules open and close the pooling chain lines for selecting and grouping modules. Analog data lines separate from the pooling chain can also carry data to and from the modules. Pooling control lines connected to the switches turn the switches on and off for the selecting and grouping of modules. Module control lines, also separate from the pooling chain, connected to the modules enable various data input, output, and processing by the memory or other components in the module.