Hardware-Accelerated Data Parsing for Scalable Regex Enrichment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data parsing and enrichment technologies are inefficient and resource-intensive, particularly when dealing with large numbers of regular expressions, leading to suboptimal CPU resource allocation and limited scalability.
Innovation Solution
A hardware-accelerated data parser/processor utilizing a hardware accelerator (e.g., FPGA, GPU) with finite state automata to perform parallel processing of regular expressions, reducing CPU resource allocation and enabling scalable, asynchronous data parsing and enrichment through parametrically initiated threads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional CPU-based data parsing and enrichment is used, then system complexity is low, but processing speed and productivity are insufficient
Solution Approach 1:
The patent introduces a hardware accelerator as an intermediary component between the CPU and data processing tasks. The hardware accelerator specifically handles regular expression matching and data parsing operations, allowing the CPU to focus on higher-level control logic. This mediator approach resolves the contradiction by offloading computationally intensive tasks to specialized hardware, thereby increasing productivity without significantly complicating the overall system architecture.
Solution Approach 2:
The patent replaces the mechanical/software-based CPU processing system with a hardware-accelerated processing system. By substituting software regular expression matching with hardware-based finite state machines and dedicated parsing circuits, the system achieves significant speedup in data parsing operations. This substitution resolves the contradiction by trading increased hardware complexity for dramatically improved processing productivity.
2Productivity
If more CPU resources are allocated for data parsing, then processing capability improves, but resource utilization becomes suboptimal
Solution Approach 1:
The patent segments the data processing workload by separating regular expression matching and parsing tasks from general CPU operations. The hardware accelerator is divided into specialized units for different parsing functions, allowing efficient resource allocation. This segmentation resolves the contradiction by dedicating specific hardware resources to parsing tasks, improving processing capability while optimizing overall CPU resource utilization efficiency.
Solution Approach 2:
The patent changes the operational parameters of the processing system by moving from software-based to hardware-based processing. This parameter change fundamentally alters the energy-resource efficiency profile, as hardware accelerators perform parsing operations with higher efficiency and lower CPU resource consumption. The resolution comes from changing the processing paradigm rather than simply allocating more CPU resources.
3Speed
If hardware acceleration is implemented, then processing speed increases, but device complexity increases
Solution Approach 1:
The patent implements a universal hardware accelerator that can handle multiple types of data parsing and regular expression matching operations. Rather than creating separate hardware for each parsing task, the design uses a multi-functional accelerator that can be configured for different parsing scenarios. This universality resolves the contradiction by achieving high processing speed through hardware acceleration while limiting complexity growth through resource sharing and multi-functionality.
Solution Approach 2:
The patent transitions from software-based processing to hardware-based processing, effectively adding a new dimension to the system architecture. This dimensional change from software to hardware enables parallel processing and pipelining capabilities that dramatically increase speed. The resolution of the contradiction lies in this dimensional transition, where hardware implementation provides speedup while the modular architecture manages complexity.
4Productivity
If synchronous processing is used, then implementation is simpler, but system responsiveness and throughput are limited
Solution Approach 1:
The patent implements periodic action through asynchronous processing where the hardware accelerator operates independently and continuously processes data packets. Rather than waiting for synchronous coordination, the accelerator processes data at its own pace and communicates results through interrupt-driven or event-driven mechanisms. This periodic independent operation resolves the contradiction by maximizing data throughput while managing architectural complexity through event-based synchronization.
Solution Approach 2:
The patent ensures continuity of useful action by implementing an asynchronous processing pipeline where the hardware accelerator continuously processes incoming data without being blocked by CPU readiness. Multiple data packets can be processed in parallel, and the system maintains continuous throughput even when CPU resources are temporarily unavailable. This continuity resolves the contradiction by achieving high productivity through uninterrupted processing while using standard asynchronous programming patterns to manage complexity.
Data Source
AI summary
A data parser/processor and enricher accelerated by making use of at least one hardware accelerator or a finite state automata (i.e. FPGA, CPLD, GPU, SoC, NoC, ASIC, etc.) obtaining messages from a message queue/topic and extracts various information from these messages/data by parsing/processing and preferably combines with the original message and then forwards to another message queue/topic in an asynchronous way. It is in the field of systems and methods for data parsing/processing and enrichment that includes at least one hardware accelerator/finite state automata and parametrically initiated threads for processing and enriching the retrieved message in multiple cores of processors. These result in a data parser/processor or enricher that can be scaled in both horizontally and vertically for multiple areas of applications.