Physical Neural Network Inference via Adjoint Simulation
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Solution Overview
Problem
Conventional design techniques for electromagnetic, acoustic, and fluidic devices rely on a simple guess-and-check method, adjusting a limited number of parameters, which is inefficient due to the vast number of design parameters involved, often resulting in high computational and power requirements, limiting the deployment of sophisticated machine learning models to low-powered devices.
Innovation Solution
A physics simulator is used to optimize the structure of physical devices by mapping neural network inference computations to physical domains, utilizing operational and adjoint simulations to adjust structural parameters based on first-principles physics, reducing latency, computational cost, and memory usage, enabling the deployment of machine learning models on a wide range of devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional guess-and-check design techniques are used to adjust design parameters, then device design can be performed with simple methods, but the computational cost and power requirements become excessively high due to the vast number of design parameters
Solution Approach 1:
The patent replaces conventional digital computational systems with a physical device that performs neural network inference operations using physical phenomena (electromagnetic, acoustic, or fluidic). The physical device structure itself encodes the neural network architecture, allowing inference to be performed through physical signal propagation rather than digital computation, thereby dramatically reducing computational power requirements while maintaining design capability
Solution Approach 2:
The patent transforms the design approach by changing parameters from adjusting individual design parameters through guess-and-check to optimizing the physical device structure to replicate neural network behavior. The physical device's structural parameters (geometry, material properties, configuration) are optimized to encode neural network weights and architecture, enabling efficient inference without requiring high computational power during operation
2Ease of manufacture
If conventional design techniques adjust only a limited number of parameters, then the design process remains simple, but the manufacturing precision and performance optimization are insufficient due to the vast number of unexplored design parameters
Solution Approach 1:
The patent creates a physical copy or replica of the neural network architecture in the physical domain. The physical device structure replicates the neural network's computational functionality, with physical signals representing data flow and structural elements representing network components. This copying approach allows the physical device to achieve high manufacturing precision by faithfully reproducing the optimized neural network architecture without requiring exploration of all possible design parameters
Solution Approach 2:
The patent creates a universal physical device platform that can be configured to perform different neural network inference tasks. By optimizing the physical device structure to encode neural network architecture, the same physical platform can be adapted to various applications (image classification, object detection, etc.) without requiring separate design processes for each task, thereby achieving both design simplicity and high precision through a unified approach
3Reliability
If sophisticated machine learning models are deployed using conventional computational methods, then accurate inference can be achieved, but latency and memory requirements increase, limiting deployment to low-powered devices
Solution Approach 1:
The patent replaces digital computational processing with physical signal propagation to perform neural network inference. Physical signals (electromagnetic waves, acoustic waves, or fluid flow) naturally propagate through the device structure at the speed of light or equivalent physical speed, eliminating the sequential processing delays inherent in digital computation. This substitution maintains inference accuracy while dramatically reducing latency
Solution Approach 2:
The physical device performs inference operations autonomously through the natural laws of physics governing signal propagation and interaction with the device structure. The device self-encodes the neural network computation in its physical architecture, allowing it to perform inference without requiring external computational resources, memory access, or power-intensive processing, thereby reducing both latency and memory requirements while maintaining accuracy
Data Source
AI summary
A computer-implemented method for revising structural parameters of a physical device is provided. The method comprises configuring a simulated environment to be representative of the physical device based on an initial description that describes structural parameters of the physical device. The method further includes performing an operational simulation of the physical device based on training data representative of physical stimuli within a physical domain to simulate an interaction between the physical device and the physical stimuli. The method further includes computing a loss value based on a simulated output of the physical device and performing and adjoint simulation by backpropagating the loss value through the simulated environment. The method also includes generating a revised description of the physical device by updating the structural parameters to reduce the loss value.