Symbol Response Memory Validation Using Error Detection Engines
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Solution Overview
Problem
Conventional computing systems face inefficiencies in pattern recognition due to the increasing volume of data and number of patterns to be identified, leading to bottlenecks in processing speed and accuracy, particularly in detecting spam or malware, and existing hardware struggles to validate configuration content in electronic devices used for data analysis.
Innovation Solution
A state machine engine with a hierarchical structure of finite state machine lattices operates in parallel, analyzing data streams across multiple criteria simultaneously, and includes an error detection engine to validate configuration content, mimicking the biological brain's pattern recognition capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional computing systems process pattern recognition sequentially, then processing accuracy is maintained, but processing speed decreases due to increasing data volume and pattern complexity
Solution Approach 1:
The patent segments the pattern recognition task into multiple parallel finite state machine circuits, each capable of independently processing different patterns simultaneously. This segmentation enables the system to handle increasing data volume and pattern complexity without sequential processing bottlenecks, thereby improving processing speed while distributing system complexity across multiple independent units
Solution Approach 2:
The patent transitions from sequential one-dimensional processing to parallel multi-dimensional processing by organizing finite state machine circuits in a hierarchical structure with multiple levels. This dimensional change allows simultaneous processing of multiple patterns across different circuit levels, resolving the contradiction between speed and complexity
2Speed
If hardware distributes data stream among multiple circuits for parallel pattern search, then processing speed increases, but intermediate results become larger than original input data causing system bottlenecks
Solution Approach 1:
The patent extracts only the necessary pattern match results from each finite state machine circuit rather than processing and storing complete intermediate data. Each circuit outputs only the specific pattern detection results, eliminating the bottleneck of handling large intermediate result volumes while maintaining parallel processing speed advantages
Solution Approach 2:
The patent performs preliminary filtering at each circuit level where finite state machines evaluate and eliminate non-matching patterns before results are passed to the next level. This preliminary action reduces the volume of data that needs to be processed further, preventing intermediate result expansion while maintaining processing speed
3Adaptability or versatility
If configuration content is stored in electronic devices for data analysis, then pattern recognition capability is enabled, but bit failure or corruption may alter configuration content reducing reliability
Solution Approach 1:
The patent implements feedback mechanisms where configuration content is continuously validated and corrected. Error detection circuits monitor the configuration data stored in finite state machines, and when bit failures or corruption are detected, the system generates feedback signals to request retransmission or correction of the configuration content, thereby maintaining reliability while preserving pattern recognition capability
Solution Approach 2:
The patent employs error detection and validation circuits that are预先 built into the system to cushion against configuration content corruption. These circuits continuously verify the integrity of configuration data before it is used for pattern recognition, preventing corrupted data from affecting system reliability while maintaining full pattern recognition functionality
Data Source
AI summary
Configuration content of electronic devices used for data analysis may be altered due to bit failure or corruption, for example. Accordingly, in one embodiment, a device includes a plurality of blocks, each block of the plurality of blocks includes a plurality of rows, each row of the plurality of rows includes a plurality of configurable elements, each configurable element of the plurality of configurable elements includes a data analysis element including a memory component programmed with configuration data. The data analysis element is configured to analyze at least a portion of a data stream based on the configuration data and to output a result of the analysis. The device also includes an error detection engine (EDE) configured to perform integrity validation of the configuration data.