FPGA Rule Evaluation for Low-Latency Stream Processing
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Solution Overview
Problem
Conventional business rule processing systems face challenges with large and growing rule sets and data volumes, leading to high latency and potential bottlenecks in decision-making processes, especially when handling high-speed data streams.
Innovation Solution
The implementation of a hardware-accelerated rule condition check system using a coprocessor, such as a field programmable gate array (FPGA), which streams data and generates rule condition check results at bus bandwidth rates, reducing latency by performing operations like matching, filtering, and aggregation directly on incoming data streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional business rule processing systems are used, then system simplicity is maintained, but processing latency increases and productivity decreases
Solution Approach 1:
The system segments rule processing into two distinct parts: a reconfigurable logic device (FPGA) that handles high-speed pattern matching and rule evaluation, and a conventional processor that manages rule set updates and complex control logic. This segmentation allows the time-critical matching operations to run in parallel hardware while keeping the overall system architecture manageable through clear separation of concerns.
Solution Approach 2:
The patent introduces a reconfigurable logic device as an intermediary component between the conventional processor and the data stream. This intermediary handles the high-speed pattern matching and rule evaluation, acting as a buffer that prevents the conventional processor from becoming a bottleneck, thereby reducing overall processing latency without requiring complete system redesign.
2Adaptability or versatility
If rule set size increases to handle more business requirements, then adaptability improves, but processing speed decreases
Solution Approach 1:
The reconfigurable logic device allows the rule processing architecture to dynamically adapt to different rule set sizes and complexities. The device can be reconfigured via firmware to accommodate varying rule sets without hardware changes, enabling the system to maintain high processing speeds while handling growing rule sets through dynamic reconfiguration rather than static design.
Solution Approach 2:
The system changes the parameter of rule processing from software-based sequential evaluation to hardware-based parallel evaluation. By altering the fundamental processing parameter from software instructions to hardware logic circuits, the system can evaluate multiple rules simultaneously, maintaining processing speed even as the number of rules increases to meet growing business requirements.
3Productivity
If data stream speed increases to meet real-time requirements, then responsiveness improves, but system bottleneck risk increases
Solution Approach 1:
The patent replaces the mechanical/software-based rule processing system with a hardware-based reconfigurable logic device. This substitution transforms the processing mechanism from sequential software execution to parallel hardware evaluation, enabling the system to handle high-speed data streams without creating bottlenecks, as the hardware architecture is specifically designed to process multiple data elements simultaneously at line rate.
Data Source
AI summary
Disclosed herein are methods and systems for hardware-accelerating various data processing operations in a rule-based decision-making system such as a business rules engine, an event stream processor, and a complex event stream processor. Preferably, incoming data streams are checked against a plurality of rule conditions. Among the data processing operations that are hardware-accelerated include rule condition check operations, filtering operations, and path merging operations. The rule condition check operations generate rule condition check results for the processed data streams, wherein the rule condition check results are indicative of any rule conditions which have been satisfied by the data streams. The generation of such results with a low degree of latency provides enterprises with the ability to perform timely decision-making based on the data present in received data streams.