Interconnection Fabric Synthesis via Port Adapter Constraints
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Solution Overview
Problem
Existing methods for generating interconnection fabrics in programmable chip systems often fail to meet user-specific performance constraints such as throughput, latency, and power consumption, leading to inefficient system designs and potential performance degradation.
Innovation Solution
A system tool that receives user-defined constraints to intelligently select and connect port adapter components, generating a customized interconnection fabric that optimizes system performance by varying parameters like clock frequency, arbitration priorities, and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a system tool intelligently selects and connects port adapter components using performance constraints, then system performance is optimized and resource usage is improved, but the complexity of the system generation process increases
Solution Approach 1:
The system tool automatically selects and connects port adapter components based on performance constraints without requiring manual intervention. The tool receives constraints such as throughput, latency, and power consumption, then autonomously generates an optimized interconnection fabric configuration, allowing the system to serve itself rather than requiring expert manual design.
Solution Approach 2:
The system varies parameters like clock frequency, arbitration priorities, and resource allocation to optimize performance while meeting user constraints. By dynamically adjusting these parameters during the generation process, the tool can achieve different performance trade-offs and select the optimal configuration for each specific constraint set.
2Use of energy by moving object
If higher latency components are used in place of lower latency components, then power consumption is reduced, but system speed deteriorates
Solution Approach 1:
The system dynamically selects components and configures the interconnection fabric based on performance constraints, allowing it to adapt between different latency and power consumption trade-offs. Rather than using a fixed component selection, the tool can choose higher latency components when power consumption is the primary constraint, or lower latency components when speed is prioritized.
Solution Approach 2:
The system changes operational parameters such as clock frequency and component selection to achieve different performance points. By varying these parameters, the tool can optimize for either power consumption or speed depending on the user's specific constraints and requirements.
3Productivity
If dedicated port adapters are used instead of shared port adapters, then throughput is improved, but resource usage increases
Solution Approach 1:
The system can configure port adapters to serve multiple functions or be shared among different components based on performance constraints. When throughput is not the primary constraint, shared port adapters can serve multiple masters or slaves, reducing overall resource usage while still meeting performance requirements through intelligent allocation and arbitration.
Solution Approach 2:
The system dynamically determines whether to use dedicated or shared port adapters based on the specific performance constraints provided by the user. Rather than a fixed architecture, the tool can adapt the port adapter configuration to achieve the optimal balance between throughput and resource usage for each specific design scenario.
Data Source
AI summary
Methods and apparatus are provided for receiving performance constraints for implementing a system. A system tool receives constraints such as throughput, latency, power consumption, resource usage, etc. and generates an interconnection fabric using the constraint information. The interconnection fabric includes ports adapters used to connect master components and slave components. In some instances, port adapters and components are intelligently selected and connected using the constraint information.