Camera Device GPU Integration for Real-Time CNN Feature Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image capturing devices lack integration with deep machine learning capabilities, particularly Convolutional Neural Networks (CNNs), which hinders real-time feature detection and increases computational intensity during image processing.
Innovation Solution
Integrating a graphics processing unit (GPU) with a camera device to accelerate graphics and machine-learning operations, enabling real-time CNN processing and storage of deep channel images that combine RGB and CNN channel data, thereby reducing computational load and enhancing feature detection capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a camera device is integrated with CNN processing capabilities, then feature detection capability is improved, but device complexity increases
Solution Approach 1:
The patent merges the camera device with a graphics processing unit (GPU) that has dedicated CUDA cores for parallel processing. This integration combines image capture functionality with CNN computation capabilities into a unified system, enabling feature detection directly at the camera while managing complexity through shared hardware resources and memory spaces.
Solution Approach 2:
The GPU in the integrated system serves multiple functions: it processes traditional graphics rendering tasks while simultaneously executing CNN algorithms for feature detection. This multi-functionality allows a single component to handle diverse computational workloads, improving feature detection capability without proportionally increasing overall device complexity.
2Productivity
If CNN processing is performed in real-time during image capturing, then processing efficiency is improved, but computational intensity increases
Solution Approach 1:
The CNN processing is segmented into multiple layers with feature maps generated at each layer. The system processes images through successive convolutional layers, pooling operations, and activation functions in a staged manner. This segmentation allows incremental computation and memory management, improving processing efficiency while distributing computational intensity across time and hardware resources.
Solution Approach 2:
The system dynamically adjusts processing parameters including batch sizes, learning rates, and memory allocation based on real-time computational demands. The GPU utilizes dynamic memory management and parallel thread execution to optimize computational intensity, enabling real-time processing by adapting resource utilization to the specific requirements of each CNN layer and operation.
Data Source
AI summary
Methods and systems are disclosed using camera devices for deep channel and Convolutional Neural Network (CNN) images and formats. In one example, image values are captured by a color sensor array in an image capturing device or camera. The image values provide color channel data. The captured image values by the color sensor array are input to a CNN having at least one CNN layer. The CNN provides CNN channel data for each layer. The color channel data and CNN channel data is to form a deep channel image that stored in a memory. In another example, image values are captured by sensor array. The captured image values by the sensor array are input a CNN having a first CNN layer. An output is generated at the first CNN layer using the captured image values by the color sensor array. The output of the first CNN layer is stored as a feature map of the captured image.