Sensor-Side Dimensionality Reduction for Deep Learning Imaging
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Solution Overview
Problem
Existing deep learning architectures inefficiently utilize computational resources due to the sequential processing of raw sensor data, which is scaled and downsized without considering relevance to the learning task, leading to suboptimal resource usage.
Innovation Solution
Implementing a method for acquiring reduced-dimensionality images by leveraging learned dimensionality reduction through sensors, using techniques like PCA, ICA, and SFA, to reduce data bandwidth and computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If raw sensor data is processed through standard image acquisition pipeline (scaling and downsizing), then computational burden on neural network is reduced, but computational resources are inefficiently utilized and information loss occurs
Solution Approach 1:
The patent applies preliminary action by performing dimensionality reduction on sensor data before it enters the neural network pipeline. The system proactively reduces the dimensionality of sensor data at the acquisition stage, transforming raw sensor data into a compressed representation that retains essential information while reducing computational burden downstream, thereby resolving the contradiction between reducing computational load and maintaining resource efficiency
Solution Approach 2:
The patent changes the parameter of data dimensionality by transforming raw sensor data into a lower-dimensional representation. This parameter change is achieved through dimensionality reduction techniques that compress the data while preserving relevant information, allowing the system to reduce computational burden without sacrificing the efficiency of resource utilization
2Speed
If image is scaled and downsized to predetermined square size, then deep learning inference speed is improved, but original geometry and orientation information is discarded
Solution Approach 1:
The patent extracts only the essential information from the original sensor data through dimensionality reduction. Instead of discarding geometry information through standard scaling and downsizing, the system extracts and retains only the critical features and spatial relationships needed for deep learning, thereby achieving fast inference while preserving meaningful geometric information
Solution Approach 2:
The patent applies dimensionality change by transforming data from high-dimensional raw sensor space into a optimized lower-dimensional representation. This dimensional transformation is achieved through learned dimensionality reduction that compresses data while maintaining the essential geometric and orientational information in a compact format, enabling fast inference without information loss
3Ease of manufacture
If standard image acquisition pipeline is used, then image processing is simplified, but computational requirements are not optimized for deep learning applications
Solution Approach 1:
The patent merges the image acquisition process with dimensionality reduction operations. Instead of separate sequential steps (acquisition then processing), the system combines acquisition with learned dimensionality reduction, creating an integrated pipeline that simultaneously captures and compresses data in an optimized manner for deep learning, thereby improving computational resource efficiency while maintaining processing simplicity
Solution Approach 2:
The patent implements self-service by using the neural network itself to guide the dimensionality reduction process. The learned dimensionality reduction model is trained based on the specific deep learning task requirements, allowing the system to automatically optimize data representation without external intervention, thus improving computational efficiency while keeping the processing pipeline simple
Data Source
AI summary
The invention relates, amongst others, to a method for image acquisition, comprising: acquiring a reduced-dimensionality image comprising a relevant object by means of an acquisition means comprising one or more sensors for recording raw sensor data of said relevant object: feeding said reduced-dimensionality image to a neural network trained with respect to said relevant object; wherein said acquisition means further comprises an acquisition module, preferably a hardware-implemented module, and an acquisition interface connecting said one or more sensors to said acquisition module; wherein said acquiring comprises reducing a dimensionality of said raw sensor data according to a learned dimensionality reduction learned by means of a first set of training examples, said learned dimensionality reduction comprising a bandwidth-reducing operation learned based on said first set of training examples and being performed by one or more respective ones of said sensors, said bandwidth-reducing operation resulting in an amount of data being sent by said respective sensor over said acquisition interface being less than the amount of raw sensor data being recorded by the respective sensor.