Proximal Pixel Processing Architecture for High-Density Image Sensors
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Solution Overview
Problem
Conventional image capture and processing systems face challenges with high-speed data transfer requirements, complexity, cost, power consumption, and size due to the need to move large amounts of pixel data from image sensors to CPUs and memory for processing, especially with high-resolution image sensors and complex algorithms.
Innovation Solution
A distributed, parallel image capture and processing architecture where computational circuits are placed proximal to the pixel array, performing computations in parallel on pixel values, thereby reducing the need for high-speed data transfer and allowing local processing of pixel-level operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pixel data is transferred from image sensor to CPU for processing, then image processing can be performed with high flexibility, but data transfer speed requirements and system complexity increase dramatically
Solution Approach 1:
The patent segments the image processing system into two parts: a simplified sensor node that only captures and transmits raw pixel data, and a separate processing system that performs all computational tasks. This segmentation allows the sensor to remain simple while the processing system handles complexity, resolving the contradiction between flexibility and system complexity.
Solution Approach 2:
The patent introduces a buffer memory as an intermediary between the image sensor and CPU. This buffer temporarily stores pixel data, allowing the sensor to operate independently at its own speed while the CPU processes data at its own pace, thereby reducing the complexity of high-speed direct data transfer while maintaining processing flexibility.
2Measurement precision
If high-resolution image sensors are used, then image quality improves, but the volume of data requiring transfer and processing increases
Solution Approach 1:
The patent extracts only the essential raw pixel data from the high-resolution sensor and transfers it to the processing system, leaving out any pre-processed or redundant information. This extraction approach maintains the full quality benefit of high-resolution sensors while minimizing the actual data volume that needs to be transferred and processed.
3Productivity
If data is transferred at high speed from sensor to CPU, then frame rate requirements can be met, but power consumption and timing constraints increase
Solution Approach 1:
The patent implements periodic data transfer using frame-based operation, where pixel data is accumulated over a complete frame period and then transferred in batches to the CPU. This periodic action allows the sensor to operate continuously at high frame rates while data transfer occurs at lower, more power-efficient intervals, resolving the contradiction between frame rate and power consumption.
4Loss of energy
If computational circuits are placed proximal to pixels, then data transfer requirements are reduced, but device complexity and manufacturing difficulty increase
Solution Approach 1:
The patent merges the computational circuits with the pixel array to form an integrated image sensor chip. This merging approach reduces the physical distance between pixels and processing elements, minimizing data transfer requirements and power consumption, while the integration is achieved through standard semiconductor manufacturing processes that maintain ease of production.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces timing constraints and complexity, lowers power consumption, and decreases system size by enabling efficient parallel processing close to the pixels, thus improving the performance and cost-effectiveness of image capture and processing systems.
Implementation Method 1
Each pixel sensor is operative to generate a pixel value in response to incident photons
Data Source
AI summary
A distributed, parallel, image capture and processing architecture provides significant advantages over prior art systems. A very large array of computational circuits—in some embodiments, matching the size of the pixel array—is distributed around, within, or beneath the pixel array of an image sensor. Each computational circuit is dedicated to, and in some embodiments is physically proximal to, one, two, or more associated pixels. Each computational circuit is operative to perform computations on one, two, or more pixel values generated by its associated pixels. The computational circuits all perform the same operation(s), in parallel. In this manner, a very large number of pixel-level operations are performed in parallel, physically and electrically near the pixels. This obviates the need to transfer very large amounts of pixel data from a pixel array to a CPU/memory, thus alleviating the significant high-speed performance constraints placed on modern image sensors.