State Machine Engine Programming for Parallel Pattern Recognition

The patent divides the pattern recognition system into multiple finite state machine lattices organized in a hierarchical structure. Each lattice processes specific patterns independently, allowing parallel execution. This segmentation enables the system to handle multiple patterns simultaneously without creating a single point of bottleneck, thereby improving processing speed while distributing system complexity across modular units.

The patent transitions from sequential processing in conventional computers to parallel processing across multiple FSM lattices. By organizing lattices in hierarchical levels where lower levels process raw signals and higher levels process intermediate results, the system adds a dimensional aspect to computation. This multi-level parallel architecture enables simultaneous processing of multiple data streams and patterns, dramatically improving speed without linearly increasing overall system complexity.

The patent segments the pattern detection task across multiple FSM lattices, where each lattice is configured to detect specific patterns. This segmentation allows the system to process multiple patterns simultaneously in parallel rather than sequentially. When new patterns need to be detected, additional lattices can be activated or existing ones reconfigured, maintaining low processing delay while expanding detection capability.

The FSM lattices are pre-configured with pattern recognition logic before data processing begins. This preliminary configuration allows the system to be immediately ready to detect patterns as data arrives, eliminating setup delays. When new patterns need detection, the system can quickly reconfigure lattices using stored configuration data, minimizing the time loss between pattern sets.

The patent introduces a hierarchical dimension to the parallel circuit architecture, organizing FSM lattices into multiple levels. Lower-level lattices process raw data and generate intermediate results, which are then processed by higher-level lattices. This hierarchical arrangement enables complex data analysis by composing simple pattern recognitions into more sophisticated detections, mimicking the biological brain's hierarchical neural organization while maintaining high processing capacity through parallel execution at each level.

The FSM lattice architecture is designed to be universally applicable to various pattern recognition tasks. Each lattice can be configured to detect different patterns, and the hierarchical organization allows the same structural framework to handle both simple and complex analysis requirements. This multi-functionality enables the system to adapt to different data analysis needs without requiring fundamentally different hardware architectures, maintaining productivity while enhancing versatility.