State Machine Engine for Parallel Data Pattern Recognition
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Solution Overview
Problem
Current computing systems face challenges in efficiently performing complex data analysis, particularly in pattern recognition tasks, due to the inefficiencies of conventional von Neumann architecture and the inability of existing hardware to process large data volumes in real-time.
Innovation Solution
The implementation of a state machine engine with finite state machine (FSM) lattices arranged in a hierarchical parallel configuration, allowing for simultaneous analysis of data streams across multiple criteria, and the use of data padding to optimize data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional von Neumann architecture is used for data analysis, then system simplicity is maintained, but processing speed and efficiency deteriorate
Solution Approach 1:
The system segments data analysis into multiple parallel Finite State Machine (FSM) lattices, each capable of independently processing different patterns simultaneously. This segmentation enables concurrent pattern recognition without the sequential bottlenecks of conventional von Neumann architecture, thereby improving processing speed while distributing complexity across modular units.
Solution Approach 2:
The patent transitions from sequential processing in a single dimension to parallel processing across multiple dimensional layers of FSM lattices. By organizing state machines in hierarchical layers that process data simultaneously, the system achieves exponential speedup for complex pattern recognition tasks while maintaining manageable complexity through structured organization.
2Productivity
If data is searched for each pattern one at a time, then system simplicity is maintained, but processing time and delays increase
Solution Approach 1:
Multiple pattern recognition operations are merged into a single parallel processing framework where FSM lattices evaluate multiple patterns simultaneously against the same data stream. This merging eliminates the sequential delays of traditional approaches while maintaining system coherence through shared state management and coordinated output aggregation.
Solution Approach 2:
The FSM lattice architecture enables continuous processing of data streams without interruption for pattern switching. Once the system is initialized with multiple patterns, it continuously analyzes incoming data against all patterns simultaneously, eliminating the start-stop delays inherent in sequential pattern searching and maintaining constant productive action.
3Measurement precision
If hardware is configured to search data streams for patterns, then pattern recognition capability is improved, but the ability to process adequate data volumes in given time deteriorates
Solution Approach 1:
The system creates multiple copies of the pattern recognition functionality through replicated FSM lattices, where each lattice maintains the full pattern detection capability. This copying approach allows the system to process multiple data streams or evaluate multiple patterns simultaneously without sacrificing detection accuracy, thereby increasing overall data processing volume while preserving measurement precision.
Data Source
AI summary
A data analysis system to analyze data. The data analysis system includes a data buffer configured to receive data to be analyzed. The data analysis system also includes a state machine lattice. The state machine lattice includes multiple data analysis elements and each data analysis element includes multiple memory cells configured to analyze at least a portion of the data and to output a result of the analysis. The data analysis system includes a buffer interface configured to receive the data from the data buffer and to provide the data to the state machine lattice.