Hardware Distributed Architecture for Data Transform Accelerators
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Solution Overview
Problem
Existing network processing systems face challenges in balancing performance, scalability, interoperability, functional flexibility, power efficiency, and cost, with current architectures like HPA and FDA either sacrificing scalability and flexibility or increasing costs and power consumption.
Innovation Solution
A hardware distributed architecture (HDA) that includes multiple packet processing components connected via a system communication channel, with a queueing system determining processing paths and dynamically scaling and reconfiguring resources to optimize performance and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a hardware pipe architecture (HPA) is used to improve performance and power efficiency, then processing speed and power performance are improved, but scalability and functional flexibility are decreased
Solution Approach 1:
The system is divided into multiple independent packet processing components (PPCs), each capable of performing specific packet processing functions. These components can be individually configured and dynamically added or removed from the processing pipeline, enabling both high-performance processing and flexible adaptation to different functional requirements.
Solution Approach 2:
The HDA employs dynamic resource allocation where the queueing system can reconfigure the number and type of PPCs in the processing pipeline based on real-time traffic patterns and processing requirements. This dynamic adaptability allows the system to maintain high performance while adjusting functional flexibility as needed.
2Adaptability or versatility
If a firmware distributed architecture (FDA) is used to improve scalability and functional flexibility, then adaptability is improved, but power consumption and cost increase
Solution Approach 1:
The system replaces firmware-based packet processing with hardware-accelerated processing using dedicated packet processing components. This substitution of mechanical/firmware operations with hardware operations reduces power consumption while maintaining the scalability and flexibility benefits of a distributed architecture.
Solution Approach 2:
Different packet processing components are specialized for specific processing functions (e.g., classification, filtering, transformation). This local specialization allows each component to operate efficiently for its designated function, reducing overall power consumption compared to general-purpose firmware processing while maintaining high functional flexibility.
3Productivity
If more packet processing components are added to improve performance and functionality, then productivity and adaptability are improved, but device complexity and cost increase
Solution Approach 1:
The packet processing components are designed with universal interfaces and standardized communication protocols, allowing them to be added to the system without proportionally increasing complexity. The queueing system provides a unified management layer that handles resource allocation and coordination, enabling the system to scale by adding components rather than redesigning the entire architecture.
Data Source
AI summary
A method includes obtaining data to process using at least one data transform operation. The method further includes determining a processing path for the data to traverse at least a first data transform engine and a second data transform engine. The method also includes directing the data to the first data transform engine. The first data transform engine is to perform a first data transform operation on the data. The method further includes directing the data to the second data transform engine, the second data transform engine to perform a second data transform operation on the data.