Fixed Retail Scanner with Distributed AI Accelerator Modules
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Solution Overview
Problem
Existing retail scanners lack efficient distributed AI acceleration capabilities, which limits their ability to perform complex image analysis and data processing in real-time, especially in environments with diverse optical codes and images.
Innovation Solution
A fixed retail scanner equipped with distributed on-board AI accelerator modules, including local imager AI engines and a system AI engine, that work in conjunction with a system processor to schedule and dispatch AI tasks across a network of AI resources, enabling efficient processing of image data from multiple camera modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If distributed AI accelerator modules are added to the scanner, then AI processing capability is improved, but device complexity increases
Solution Approach 1:
The AI processing system is segmented into multiple independent AI accelerator modules distributed across different locations within the scanner. Each module can independently execute AI tasks, allowing the system to achieve high AI processing capability while maintaining modular architecture that manages complexity through division of functions.
Solution Approach 2:
The AI accelerator modules are designed with universal functionality to handle diverse AI tasks including optical code recognition, image analysis, and object identification. This multi-functionality allows a single module architecture to serve multiple purposes, improving AI capability without proportionally increasing overall system complexity.
2Productivity
If multiple AI engines are deployed across distributed modules, then AI task processing efficiency is improved, but system architecture complexity increases
Solution Approach 1:
The system architecture is segmented into discrete AI accelerator modules with clearly defined communication interfaces. Each module operates semi-independently, processing AI tasks locally while communicating results to the central system. This segmentation enables parallel processing of multiple AI tasks simultaneously, improving efficiency while the modular interface design keeps architecture complexity manageable.
Solution Approach 2:
A central system processor acts as an intermediary that coordinates tasks between multiple distributed AI engines. It receives image data, distributes appropriate tasks to available AI engines, and aggregates results. This mediator approach enables efficient parallel processing while simplifying the overall architecture by providing a single point of coordination rather than requiring complex peer-to-peer communication between all components.
3Measurement precision
If real-time AI analysis is performed on diverse optical codes and images, then scanning accuracy is improved, but processing time increases
Solution Approach 1:
The scanning and processing system is segmented so that initial optical code recognition is performed by dedicated code readers, while more complex image analysis tasks are offloaded to distributed AI accelerator modules. This segmentation allows simple tasks to be completed quickly by specialized components while complex real-time AI analysis is performed in parallel by multiple accelerators, maintaining both high accuracy and fast processing.
Solution Approach 2:
The system performs partial AI analysis on subsets of image data in parallel across multiple accelerators rather than analyzing the complete dataset sequentially. Each AI engine processes a portion of the visual field or a specific type of recognition task, achieving comprehensive scanning accuracy through aggregated partial results while significantly reducing total processing time through parallel execution.
Data Source
AI summary
The disclosure includes a fixed retail scanner includes a data reader. The data reader includes a main board including a system processor disposed within the data reader, and one or more camera modules disposed within the data reader and operably coupled with the system processor. Each camera module may include a local on-board imager AI engine configured to perform AI tasks according to a loaded trained AI model. A system artificial intelligence (AI) engine may be disposed within the data reader and configured to perform AI tasks according to a loaded trained AI model. The system processor is operably coupled to each of the imager AI engines and the system AI engine for scheduling and dispatching AI tasks across a distributed network of AI resources including the imager AI engines and the system AI engine.