Pixel-Parallel CMOS Image Sensor with Attention-Based Preprocessing for CNN Inference
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Solution Overview
Problem
High image resolution in IoT devices leads to data deluge, quality of service issues, and high energy expenditure due to computationally expensive operations like CNNs, where traditional sequential processing methods fail to meet time-critical applications and low-power requirements.
Innovation Solution
A hierarchical architecture for smart image sensors with an attention-based preprocessing layer and inference computation layers that perform pixel-parallel operations, using predictive coding and edge detection to identify relevant regions and reduce redundant data processing, enabling efficient CNN operation near the image sensor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional sequential processing methods are used for CNN operations, then device complexity remains manageable, but processing speed and time-critical performance deteriorate
Solution Approach 1:
The patent segments the image processing task into two distinct parts: (1) pixel-parallel operations performed at the sensor level for attention-based preprocessing, and (2) sequential CNN inference executed by external processors. This segmentation allows high-speed parallel processing of relevant regions while keeping the overall system architecture manageable by offloading computationally intensive tasks to external devices.
Solution Approach 2:
The patent introduces an intermediary architecture where the image sensor with integrated processing units acts as a mediator between image acquisition and external processing. The sensor performs attention-based preprocessing and relevant region identification before transferring data to external processors for complete CNN inference, enabling speedup without requiring full CNN integration at the sensor level.
2Measurement precision
If high image resolution is used to improve quality, then measurement precision improves, but data volume and processing time increase
Solution Approach 1:
The patent extracts only the relevant regions from high-resolution images using attention-based preprocessing at the pixel level. By identifying and processing only the most informative regions rather than the entire high-resolution image, the system maintains measurement precision for critical areas while significantly reducing processing time and data volume for subsequent analysis.
Solution Approach 2:
The patent performs preliminary attention-based preprocessing at the pixel level before complete image analysis. This preliminary action identifies relevant regions and prepares them for efficient processing, enabling the system to handle high-resolution images faster by pre-filtering and prioritizing important information before main processing begins.
3Speed
If fine-grain processors are integrated with pixel circuits to enable pixel-parallel operation, then processing speed improves, but device area and fill factor deteriorate
Solution Approach 1:
The patent applies partial pixel-parallel processing by implementing fine-grain processors only for attention-based preprocessing operations rather than complete CNN inference. This partial action enables pixel-parallel speedup for relevant region identification while avoiding the area penalty of integrating full computational pipelines in every pixel circuit.
Solution Approach 2:
The patent uses the image sensor as an intermediary device that performs limited pixel-parallel operations for attention-based preprocessing, then offloads the remaining computationally intensive CNN inference to external processors. This intermediary approach enables pixel-parallel speedup for critical tasks without requiring full processor integration at the pixel level, thus preserving fill factor.
4Speed
If complete CNN inference is performed at the sensor level, then processing speed improves, but energy consumption and computational complexity increase
Solution Approach 1:
The patent segments CNN inference into two parts: attention-based preprocessing performed at the sensor level for relevant region identification, and complete CNN inference executed by external processors. This segmentation enables fast processing of critical tasks at the sensor while avoiding the excessive energy consumption of running full CNN operations continuously at the pixel level.
Solution Approach 2:
The patent applies partial CNN inference at the sensor level by implementing only the attention-based preprocessing layer rather than complete CNN inference. This partial action achieves speedup for relevant region identification while significantly reducing energy consumption compared to executing full CNN operations at the sensor level.
Data Source
AI summary
Methods and systems are directed to enabling CNN operation close to an image sensor and facilitating CNN inference near the image sensor. The system includes, in part, a digital image sensor and a plurality of processing layers that include, in part, an attention-based preprocessing layer (APL), an inference computation layer (ICL), and a fully connected layer. The digital image sensor can include, in part, a plurality of regions and each region can be processed by region processing units disposed in the APL/ICL in parallel. Each region processing unit disposed in the APL can include, in part, an attention module that determines whether the corresponding region is a relevant region. The region processing units disposed in the APL transmit only relevant data to corresponding region processing units in the ICL and only activate the corresponding region processing units in the ICL for relevant regions. The ICL can include, in part, two convolution layers.