Automated NoC Placement Constraint Generation and Congestion Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Designing an optimal network-on-chip (NoC) topology that meets performance requirements such as connectivity, latency, and power consumption is complex and time-consuming, often resulting in invalid optimizations when the placement changes, and lacks real-time feedback on wire congestion during editing.
Innovation Solution
A design tool that automatically generates placement constraints for network elements, inserts additional elements for timing closure, and provides real-time wire congestion feedback, using machine learning models to optimize region creation and placement within given bounds, ensuring compatibility with backend tools and minimizing runtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual optimization of NoC topology is performed to meet performance requirements, then connectivity and latency requirements can be satisfied, but the design process becomes complex and time-consuming
Solution Approach 1:
The system performs preliminary actions by automatically generating placement constraints and inserting adapter elements before the final placement is completed. The tool pre-calculates optimal positions for network elements and pre-inserts timing closure elements, so that when the backend tool executes, the placement is already optimized and constraints are pre-established, significantly reducing the overall design time while maintaining reliability requirements.
Solution Approach 2:
The system implements feedback mechanisms by providing real-time wire congestion feedback to the user during editing. This allows the designer to see the immediate impact of placement changes on wire congestion, enabling iterative optimization without time-consuming manual analysis. The feedback loop continues until the design meets all performance requirements.
2Manufacturing precision
If placement constraints are manually created to guide backend tools, then placement accuracy can be improved, but the complexity of the design tool increases
Solution Approach 1:
The system performs self-service by automatically generating placement constraints and inserting adapter elements without requiring manual intervention for each constraint. The tool autonomously analyzes the floorplan, identifies optimal positions for network elements, and automatically creates the necessary constraints and timing closure elements, reducing the complexity burden on the designer while maintaining high placement accuracy.
Solution Approach 2:
The system changes parameters automatically by adjusting placement constraints, wire routing, and adapter insertions based on detected congestion patterns and performance requirements. Instead of manually tweaking each parameter, the system automatically modifies placement coordinates, constraint values, and timing closure parameters to achieve optimal results.
3Area of stationary object
If topology optimization is performed to meet physical constraints, then area utilization is improved, but timing closure becomes more difficult to achieve
Solution Approach 1:
The system performs preliminary timing closure actions by automatically inserting adapter elements and timing adjustment components during the initial placement phase. These preliminary insertions ensure that timing requirements are addressed before final routing and optimization, allowing the design to meet both area utilization and timing closure requirements simultaneously without requiring post-optimization adjustments.
Solution Approach 2:
The system applies local quality by inserting timing closure elements and adapters at specific local positions where congestion or timing issues are detected, rather than applying uniform adjustments across the entire design. This localized approach maintains optimal area utilization in most regions while addressing timing closure requirements at critical locations.
4Productivity
If real-time wire congestion feedback is provided during editing, then design optimization can be performed early on, but the tool requires additional computational resources
Solution Approach 1:
The system applies partial action by providing real-time congestion feedback only for critical wire segments and key design regions rather than performing exhaustive analysis of the entire floorplan. This selective feedback approach maintains high productivity for optimization while reducing the computational resources required compared to a complete real-time analysis of all wires and regions.
Data Source
AI summary
A tool is disclosed that automatically generates constraints for the placement of network elements, which can be understood by the backend tools that follow the constraints. The tool also constrains the placement within given bounds results in faster runtimes in the backend tools. Further, the tool automatically inserts additional elements into the topology to help with timing closure in downstream or backend tools. Additionally, the tool provides real-time feedback on wire congestion to the user during editing. The tool also implements a machine learning model that is trained and receives feedback for solutions provided to further train the model. The tool also includes the ability to use feedback to train a machine learning model for automated and assisted topology analysis and synthesis. The tool generates physical implementation guidance, which is during physical implementation of the synthesized NoC.