Hierarchical FSM Lattices for Real-Time Pattern Recognition
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Solution Overview
Problem
Current computing systems face inefficiencies in performing complex pattern recognition tasks due to the limitations of conventional von Neumann architecture, which struggles to process large volumes of data effectively for identifying multiple patterns in real-time.
Innovation Solution
The implementation of a state machine engine with a hierarchical configuration of finite state machine (FSM) lattices, allowing for parallel processing and cascading of data streams to enhance pattern recognition capabilities, similar to the hierarchical organization of neurons in the human brain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional von Neumann architecture is used for pattern recognition, then system simplicity is maintained, but processing speed and efficiency deteriorate when handling large volumes of data
Solution Approach 1:
The system segments the monolithic von Neumann architecture into multiple specialized processing units (neural network accelerators, FPGA devices, GPU devices) that operate in parallel. Each unit handles specific pattern recognition tasks, dividing the overall processing workload to achieve higher throughput without requiring a complete architectural overhaul of the entire system.
Solution Approach 2:
The patent introduces a hierarchical processing dimension by organizing multiple processing devices across different levels (CPU level, accelerator level, circuit level). This multi-dimensional architecture allows data to be processed simultaneously at multiple abstraction levels, effectively adding a temporal and spatial dimension to the traditional single-level von Neumann processing model.
2Measurement precision
If multiple patterns are searched sequentially in a data stream, then each pattern can be detected accurately, but processing time increases and real-time recognition becomes difficult
Solution Approach 1:
The system performs preliminary actions by pre-configuring multiple pattern matching circuits and state machines before data arrival. Each processing unit is pre-programmed with specific patterns to detect, allowing immediate parallel processing upon data receipt rather than sequential pattern loading and matching, thus eliminating setup delays for each pattern search.
Solution Approach 2:
The patent creates multiple copies of pattern matching functionality across different processing units (FPGAs, GPUs, neural accelerators). Each unit contains replicated pattern recognition logic that can independently process different portions of the data stream simultaneously, enabling multiple patterns to be detected in parallel without interfering with each other's accuracy.
3Productivity
If hardware distributes data stream among multiple parallel circuits for pattern searching, then processing speed improves, but intermediate results become larger than original input data causing system bottlenecks
Solution Approach 1:
The system extracts only the essential matching results from each parallel circuit rather than transmitting all intermediate processing data. Each processing unit outputs only the final pattern match indicators (presence/absence of detected patterns) rather than complete intermediate state information, significantly reducing the volume of data that needs to be aggregated and processed further.
Solution Approach 2:
Instead of having parallel circuits process the entire data stream and then aggregate all intermediate results, the system inverts the approach by having each circuit independently produce compressed result sets that are then combined. The aggregation happens at the result level rather than the data level, reducing intermediate data volume while maintaining processing parallelism.
Data Source
AI summary
A system includes a primary device comprising a first state machine lattice comprising a first plurality of configurable elements configured to analyze at least a portion of first data as a first analysis and to output a result of the first analysis. The system also includes a secondary device coupled to the primary device, wherein the secondary device comprises a second plurality of configurable elements configured to analyze at least a portion of second data received from the primary device as a second analysis and to output a result of the second analysis, wherein the primary device