Modular Optical Neural Network Chassis for Algorithm Adaptation
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Solution Overview
Problem
Optical neural networks (ONNs) are limited by their static hardware architecture, making it difficult to update or modify deep learning algorithms after deployment, which restricts their adaptability to new tasks or improved processing capabilities.
Innovation Solution
Incorporating a modular chassis component that allows for the integration of additional optical components or networks, enabling the adaptation of the ONN architecture by inserting modular network components before, after, or between existing layers, thereby updating the deep learning algorithm implemented by the ONN.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ONN uses static hardware architecture, then processing speed and energy efficiency are improved, but adaptability to new tasks and algorithms deteriorates
Solution Approach 1:
The ONN architecture is divided into modular components including plug-in modules that can be independently added, removed, or replaced. Each module represents a discrete functional unit that can be configured to implement different neural network layers or operations, enabling task adaptation without redesigning the entire system.
Solution Approach 2:
The system transitions from a fixed static architecture to a dynamic reconfigurable architecture. The modular components allow the system to adapt its structure in real-time or between tasks, enabling the same hardware to efficiently process different deep learning algorithms and data types through physical reconfiguration rather than software alone.
2Adaptability or versatility
If ONN architecture is modified to improve adaptability, then versatility is improved, but device complexity increases
Solution Approach 1:
The modular components are designed with universal interfaces and standardized connection protocols that allow the same module types to serve multiple functions across different configurations. A single module design can be reused in various positions and combinations to implement different neural network architectures, reducing the total number of unique components needed.
Solution Approach 2:
The system employs a hierarchical modular structure where smaller functional modules can be nested within larger assembly modules. This nesting approach allows complex functionality to be built from simpler sub-components, managing system complexity through organized hierarchy while maintaining reconfigurability at multiple levels.
3Adaptability or versatility
If traditional computing devices are used, then reconfigurability is improved, but energy consumption increases
Solution Approach 1:
The system replaces electronic reconfiguration mechanisms with optical-mechanical modular components. Instead of using software-based or electronically reconfigurable systems that consume significant power, the patent uses physically pluggable optical modules that can be configured mechanically and then operate in low-power optical processing mode for the duration of their designated task.
Data Source
AI summary
Embodiments described herein relate to an adaptable photonic apparatus including an optical neural network. The photonic apparatus includes an optical input that provides an optical signal. The photonic apparatus also includes a chassis component and an optical neural network (ONN). The chassis component includes at least one modular mounting location for receiving a modular network component. The ONN is operably connected with the optical input and is configured to perform optical processing on the optical signal according to a deep learning algorithm. The ONN includes optical components arranged into layers to form the ONN. The modular network component is an additional optical processing component that is configured to function in cooperation with the ONN to adapt the deep learning algorithm.