Daisy Chain Inference Devices for Object Identification
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Solution Overview
Problem
General users lack sufficient data sources for effective intelligent learning and object identification, limiting the application of machine learning techniques across various data sources.
Innovation Solution
An inference method and system that utilizes multiple inference devices connected in a daisy chain configuration to perform inference operations on input signals, generating and superimposing inference information for object identification, which can be integrated into displays or used externally, allowing for format conversion and reporting back to external devices for further applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple inference devices are connected in daisy chain configuration to perform inference operations on input signals, then object identification capability across different data sources is improved, but device complexity increases
Solution Approach 1:
The system divides the inference processing into multiple independent inference devices, each capable of performing inference operations on different data sources. Each device processes specific portions of the overall inference task, allowing the system to handle diverse data types and sources through modular, segmented processing units connected in daisy chain configuration.
Solution Approach 2:
Each inference device is designed with universal functionality to perform inference operations on various types of input signals from different data sources. The devices can process multiple data formats and types, making them adaptable to different identification tasks while maintaining a standardized interface for daisy chain connection.
2Ease of operation
If inference devices are integrated into displays or used externally with flexible connection, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The inference devices are designed with universal interfaces that allow them to function both as standalone external devices and as integrated components within display systems. The standardized connection protocol enables the same device to be operated in multiple configurations without requiring different hardware designs.
Solution Approach 2:
The system employs standardized interface protocols as intermediaries between the inference devices and various data sources or display systems. This intermediary layer simplifies integration by providing a uniform method for connection and communication, reducing the complexity of integrating different components.
3Productivity
If multiple inference devices are connected to achieve daisy chain function, then productivity is improved through parallel inference operations, but device complexity increases
Solution Approach 1:
The inference processing capacity is segmented across multiple devices connected in daisy chain, allowing parallel execution of inference operations on different data sources or data types. Each device handles specific processing tasks simultaneously, increasing overall productivity through distributed parallel computation.
Solution Approach 2:
Multiple inference devices are merged into a unified daisy chain system where each device contributes its processing capacity to the overall system. The devices work together as a combined system, with each unit adding its inference capabilities to the collective processing power while maintaining individual operational independence.
Data Source
AI summary
An inference method, an inference device, and a display are provided. The method includes: receiving an input signal through a first inference device or a second inference device; performing a first inference operation according to the input signal through the first inference device to obtain first inference information; performing a second inference operation according to the input signal through the second inference device to obtain second inference information; and providing an output signal according to the input signal, the first inference information and the second inference information through the second inference device.