Pattern-Matching Accelerator Sparse Coding Real-Time Classification
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Solution Overview
Problem
Traditional feature extraction methods in image processing, such as SIFT, are time-consuming and domain-specific, while deep learning algorithms, although promising, are computationally expensive, especially for real-time applications.
Innovation Solution
A compute device equipped with a pattern-matching accelerator that uses sparse coding and a dictionary of basis vectors to efficiently classify input vectors by determining sparse coding coefficients, allowing for fast comparison with basis vectors stored in associative memory, independent of their number.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional feature extraction methods like SIFT are used, then feature extraction can be performed with simple algorithms, but the processing time is excessive and not suitable for real-time applications
Solution Approach 1:
The patent segments the image processing task into multiple independent stages: gradient computation, histogram generation, and feature description. Each stage processes different aspects of the image independently, allowing for optimized computation at each step and enabling real-time processing by avoiding monolithic algorithm execution
Solution Approach 2:
The patent changes the parameter representation from traditional SIFT's complex multi-scale space to a simplified single-scale gradient histogram representation. This parameter transformation reduces computational complexity while maintaining discriminative power, enabling faster feature extraction suitable for real-time applications
2Measurement precision
If deep learning algorithms are used for image classification, then classification accuracy is improved, but computational cost becomes excessively high for real-time applications
Solution Approach 1:
The patent extracts only the most discriminative visual features (gradient orientations and magnitudes) from images, discarding redundant information. This selective extraction achieves high classification accuracy with minimal computational energy, avoiding the need for resource-intensive deep learning models while maintaining real-time performance
Solution Approach 2:
The patent uses hand-crafted gradient histogram features as a simplified copy or approximation of the complex feature representations learned by deep learning algorithms. This copied approach captures essential visual patterns with fraction of the computational cost, enabling real-time classification
Data Source
AI summary
Technologies for classification using sparse coding are disclosed. A compute device may include a pattern-matching accelerator, which may be able to determine the distance between an input vector (such as an image) and several basis vectors of an overcomplete dictionary stored in the pattern-matching accelerator. The pattern matching accelerator may be able to determine each of the distances simultaneously and in a fixed amount of time (i.e., with no dependence on the number of basis vectors to which the input vector is being compared). The pattern-matching accelerator may be used to determine a set of sparse coding coefficients corresponding to a subset of the overcomplete basis vectors. The sparse coding coefficients can then be used to classify the input vector.