Hybrid Physical Digital Neural Network Design
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Solution Overview
Problem
Existing neural network systems face challenges in balancing the advantages of digital and physical implementations, such as ease of conception and low power consumption, respectively, while struggling to achieve optimal performance in inference speed and accuracy.
Innovation Solution
A hybrid physical/digital neural network system is designed, incorporating optical and electrical components, with a computational inverse design tool optimizing the configuration of physical and digital components to maximize inference accuracy and efficiency by loosening constraints on the intermediate representation and accounting for manufacturing considerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If digital neural networks are used, then ease of conception and configurability are improved, but power consumption increases and footprint enlarges
Solution Approach 1:
The neural network system is divided into two distinct segments: physical neural network components for specific computational tasks and digital neural network components for other functions. This segmentation allows each part to operate in its optimal domain, with physical components handling low-power convolution operations and digital components providing flexibility and ease of configuration, thereby resolving the contradiction between ease of operation and power consumption.
Solution Approach 2:
The patent merges physical and digital neural network components into a hybrid system where they work together cooperatively. The physical components (such as optical or electrical devices) perform specific computations with low power consumption, while the digital components provide ease of conception and configurability. This merging allows the system to simultaneously achieve low power consumption and ease of operation.
2Use of energy by moving object
If physical neural networks are used, then power consumption decreases and footprint reduces, but configurability and ease of updates deteriorate
Solution Approach 1:
The system segments neural network functions between physical and digital components. Physical components handle fixed, low-power operations while digital components provide configurable updates. This segmentation allows the physical portion to maintain low power consumption while the digital portion provides the necessary adaptability and ease of updates.
Solution Approach 2:
The hybrid system creates a multi-functional architecture where physical components provide low-power operation and digital components provide configurability. Together, they form a universal system that can adapt to different computational needs while maintaining energy efficiency, resolving the contradiction between power consumption and adaptability.
3Adaptability or versatility
If purely digital neural networks are used, then configurability is improved, but inference speed decreases and computational power requirements increase
Solution Approach 1:
The computational workload is segmented between physical and digital components. Physical neural network components perform convolution operations at high speed with low computational power requirements, while digital components handle tasks requiring configurability. This segmentation enables the system to achieve high inference speed while maintaining adaptability.
Solution Approach 2:
The patent merges physical and digital neural network components into a hybrid architecture where physical components accelerate inference operations and digital components provide configurability. This merging allows the system to simultaneously achieve high inference speed and adaptability, resolving the contradiction between these two parameters.
4Power
If physical neural networks are used, then computational power requirements reduce, but manufacturing complexity increases
Solution Approach 1:
The system segments the neural network into physical and digital portions. The physical components, which require specialized manufacturing, are limited to specific functions where they provide the greatest benefit (low power consumption and high-speed computation). The digital components use standard manufacturing processes. This segmentation reduces overall manufacturing complexity while maintaining the computational advantages of physical components.
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 hybrid system enhances inference speed and reduces computational power requirements, allowing for increased accuracy in solving newer problems compared to traditional systems, while considering manufacturing feasibility and cost.
Implementation Method 1
optical components can be placed in front of the detector array and be configured to perform physical convolutions by using one or more physical features (e.g., dielectric surfaces, curvatures, holes, etc.)
Implementation Method 2
optical components can be placed in front of the detector array and be configured to perform physical convolutions by using one or more physical features (e.g., dielectric surfaces, curvatures, holes, etc.)
Implementation Method 3
the one or more physical components can include electrical components, such as memristors, and/or photonic integrated circuits, that can be communicatively coupled to the detector array and be configured to perform physical computations on an input signal received by the detector array
Data Source
AI summary
Systems and methods for designing a hybrid neural network comprising at least one physical neural network component and at least one digital neural network component. A loss function is defined within a design space composed of a plurality of voxels, the design space encompassing one or more physical structures of the at least one physical neural network component and one or more architectural features of the digital neural network. Values are determined for at least one functional parameter for the one or more physical structures, and the at least one architectural parameter for the one or more architectural features, using a domain solver to solve Maxwell's equations so that a loss determined according to the loss function is within a threshold loss. Final structures are defined for the at least one physical neural network component and the digital neural network component based on the values.