In-Pixel Weighting Circuits for CMOS Sensor AI Processing
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Solution Overview
Problem
Traditional CMOS Image Sensors segregate pixels, memory, and processing elements, leading to bottlenecks in size, weight, power consumption, bandwidth, and data processing, requiring data transfer to remote computing entities for AI processing, which increases energy consumption and security concerns.
Innovation Solution
Integration of a parallel transistor layer into a photosensor, allowing in-pixel processing with transistors connected in series to weight pixel outputs, enabling dot product summation and ReLU operations, and embedding memory and logic within the pixel array for efficient AI computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pixels, memory, and processing elements are separate entities in a CMOS Image Sensor, then manufacturing and design are simplified, but data transfer bandwidth is limited and energy consumption increases
Solution Approach 1:
The patent merges pixels, memory, and processing elements into an integrated in-pixel processing architecture. Processing circuits are embedded directly within each pixel unit, enabling data processing to occur at the source without requiring extensive data transfer through separate communication channels, thereby resolving the bandwidth limitation while maintaining manufacturing feasibility through systematic integration
2Ease of manufacture
If pixels, memory, and processing elements are separate entities, then component design is simplified, but energy consumption for data transfer increases
Solution Approach 1:
The patent combines processing circuits with pixel elements to create integrated in-pixel processing units. This merging eliminates the need for extensive data transfer between separate components, thereby significantly reducing energy consumption associated with data movement while maintaining component design simplicity through modular integration
3Adaptability or versatility
If data is transmitted to remote computing entities for AI processing, then processing capability is enhanced, but data transfer requirements and security concerns increase
Solution Approach 1:
The patent integrates processing circuits directly within pixel units, enabling AI processing to be performed at the sensor level. This integration enhances processing capability while simultaneously reducing data transfer infrastructure requirements, as processed results can be read out directly without requiring extensive data transmission to remote computing entities
Solution Approach 2:
The patent transitions from centralized remote processing to distributed in-pixel processing. By embedding processing capabilities within each pixel unit rather than relying on external computing entities, the system achieves enhanced processing capability while reducing the complexity of data transfer infrastructure through a dimensional shift in architectural organization
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 reduces data transfer requirements, increases processing speed, and lowers energy consumption by performing AI computations closer to the sensor, enhancing data density and reducing bottlenecks while maintaining accuracy.
Implementation Method 1
a photodiode may be connected with a gate of an amplifying transistor
Data Source
AI summary
Provided is an integrated circuit comprising: a sensor structure; a set of weighting elements, each configured to weight an output of the sensor structure; and an output element, the output element configured to collect weighted outputs of the set of weighting elements.