Development Assistance Device for ML Input Design
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Solution Overview
Problem
Conventional design assistance devices for machine learning models do not consider the design of information inputted to the inference model, limiting their ability to provide comprehensive assistance in model development.
Innovation Solution
A development assistance device that includes conversion processing circuitry for transforming signal values and meta information, pipeline processing for sequential conversion, inference processing for model inference, image generation for visualization, and output control for displaying relationships between components, enabling visualization of preprocessing and assistance in designing input information for the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional design assistance devices are used for machine learning models, then the device complexity is reduced, but the completeness of design assistance is insufficient because they do not consider the design of input information
Solution Approach 1:
The design assistance device is segmented into multiple independent functional modules: conversion processing circuitry for data transformation, pipeline processing circuitry for workflow management, inference processing circuitry for model inference, image generation processing circuitry for visualization, and output control processing circuitry for result delivery. Each module handles a specific aspect of the design process, enabling comprehensive assistance while maintaining modular complexity management.
Solution Approach 2:
The design assistance device incorporates multi-functional capabilities by integrating both data preprocessing functions (conversion processing) and model inference functions (inference processing) within a single system. The conversion processing circuitry can transform various types of input data, while the inference processing circuitry supports different inference models, providing universal design assistance across multiple tasks.
2Adaptability or versatility
If multiple conversion processing circuitries are integrated for comprehensive data preprocessing, then the completeness of information design assistance is improved, but the device complexity increases
Solution Approach 1:
The conversion processing circuitry is divided into multiple specialized units, each handling specific types of data transformations. This segmentation allows the system to provide comprehensive information design assistance for different data formats and preprocessing requirements while managing complexity through modular architecture.
Solution Approach 2:
The pipeline processing circuitry serves as an intermediary that coordinates and manages the flow of data between multiple conversion processing circuitries and the inference processing circuitry. This mediator component simplifies the overall system complexity by providing a unified interface and workflow management layer.
3Ease of operation
If visualization of preprocessing relationships is implemented, then the ease of operation is improved, but the device complexity increases due to additional image generation processing circuitry
Solution Approach 1:
The image generation processing circuitry creates visual representations (copies) of the data flow and processing relationships within the system. Instead of modifying the actual processing circuitry, it generates graphical copies that depict the preprocessing pipelines and connections, making the system easier to operate and understand without adding physical complexity to the core processing functions.
Solution Approach 2:
The image generation processing circuitry acts as an intermediary layer between the complex processing circuitry and the user interface. It translates internal data structures and processing relationships into visual formats, reducing the operational complexity perceived by users while maintaining the integrity of the underlying processing system.
Data Source
AI summary
A development assistance device includes: multiple conversion units each converting either an inputted signal value or inputted meta information into either a signal value or meta information, and outputting either the signal value after conversion or the meta information after conversion as output information; a pipeline unit causing, on the basis of pipeline information showing a relationship of mutual connections between the multiple conversion units, the multiple conversion units to sequentially perform the corresponding converting processes; an inference unit making an inference by using the output information outputted by a final conversion unit which the pipeline unit causes, on the basis of the pipeline information, to finally perform the converting process, out of the multiple conversion units; and an image generation unit generating image information for visualizing the connection relationship shown by the pipeline information, and a relationship of a connection between the final conversion unit and the inference unit.