CNN Integrated Circuit Super-Character Motion Recognition
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Solution Overview
Problem
Existing methods for recognizing motions in video clips or image sequences are inefficient as they require remote server computations, leading to time delays and security issues, and cannot be implemented in local devices due to software-based algorithms.
Innovation Solution
A method using a CNN-based integrated circuit that processes video frames by forming a 'super-character' through image classification techniques, embedding text categories as ideograms in a 2D symbol, and using convolutional neural networks to recognize motions locally within a device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If software algorithms are used for motion recognition, then motion recognition can be performed, but it cannot be implemented in local devices and requires remote server computations
Solution Approach 1:
The patent replaces software-based motion recognition algorithms with a hardware-implemented CNN-based integrated circuit. This substitution enables local device implementation by moving the computational functionality from software to dedicated hardware, eliminating the need for remote server computations while maintaining motion recognition capabilities.
2Loss of time
If remote server computations are used for motion recognition, then complex computations can be performed, but time delays occur and data security issues arise
Solution Approach 1:
The patent transitions from centralized cloud-based processing to distributed edge computing by implementing CNN circuits directly in local devices. This dimensional shift in computational architecture enables processing to occur at the edge of the network rather than requiring data transmission to remote servers, thereby eliminating time delays and improving data security by keeping data local.
3Productivity
If CNN-based integrated circuit is used for local motion recognition, then processing speed improves and data security is enhanced, but device complexity increases
Solution Approach 1:
The patent divides the CNN-based integrated circuit into multiple specialized processing units, each handling specific aspects of motion recognition. This segmentation allows the complex computational task to be distributed across multiple simpler, dedicated hardware modules, improving processing efficiency while managing device complexity through functional decomposition.
Data Source
AI summary
Two-dimensional symbols with each containing multiple ideograms for facilitating machine learning are disclosed. Two-dimensional symbol comprises a matrix of N×N pixels of data representing a “super-character”. The matrix is divided into M×M sub-matrices with each of the sub-matrices containing (N/M)×(N/M) pixels. N and M are positive integers or whole numbers, and N is preferably a multiple of M. Each of the sub-matrices represents one ideogram defined in an ideogram collection set. “Super-character” represents at least one meaning each formed with a specific combination of a plurality of ideograms. Ideogram collection set includes, but is not limited to, pictograms, logosyllabic characters, Japanese characters, Korean characters, punctuation marks, numerals, special characters. Logosyllabic characters may contain one or more of Chinese characters, Japanese characters, Korean characters. Features of each ideogram can be represented by more than one layer of two-dimensional symbol.