Fuzzy Logic Machine Learning for IC Place and Route Optimization
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Solution Overview
Problem
The challenge in integrated circuit design is optimizing the physical area of System-on-Chip (SoC) products while ensuring functional requirements are met, as existing methods require manual effort and lack consistency, and achieving the smallest possible physical area is desirable but difficult to guarantee.
Innovation Solution
Employing fuzzy logic machine learning algorithms to automatically determine the area and shape of standard cells for physical design, utilizing a partition with a netlist and proposed floorplan to minimize the required area through place and route (PnR) optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to optimize physical area, then design flexibility is maintained, but efficiency and consistency deteriorate
Solution Approach 1:
The system enables self-service optimization by allowing the design tool to automatically evaluate and optimize floorplan layouts without requiring manual intervention. The automated place-and-route algorithm autonomously determines optimal cell placements and routing configurations, making the system self-optimizing based on predefined design rules and constraints.
Solution Approach 2:
The patent replaces manual mechanical optimization processes with automated computational algorithms. Instead of designers manually adjusting layouts, the system uses computer-based place-and-route algorithms that automatically optimize the physical arrangement of cells and interconnects based on mathematical models and design rules.
2Productivity
If automated EDA processes are used, then productivity is improved, but manufacturing precision and design accuracy deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the automated EDA tool continuously evaluates design metrics (such as area, timing, and signal integrity) and adjusts the floorplan layout accordingly. The place-and-route algorithm uses feedback from design rule violations and performance metrics to iteratively improve the optimization accuracy.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting optimization parameters such as weightings for different design rules, cell placement constraints, and routing preferences. The system can modify these parameters based on the specific design requirements and technology node to maintain high precision while achieving automation.
3Area of stationary object
If the physical area is minimized, then device size is reduced, but functional requirements may be compromised
Solution Approach 1:
The system applies local quality optimization by allowing different regions of the floorplan to have different optimization characteristics. Critical areas with timing constraints or signal integrity requirements can be prioritized over areas where area minimization is the primary goal, ensuring that functional requirements are met in critical regions while achieving overall area reduction.
Solution Approach 2:
The patent implements dynamic optimization where the place-and-route algorithm can adapt its behavior based on the specific design requirements. The system dynamically adjusts the balance between area minimization and functional requirement satisfaction by weighing different design rules and constraints differently based on the project requirements, allowing flexible trade-off management.
Data Source
AI summary
Systems and methods include receiving a functional integrated circuit design and generating a plurality of place and route (PnR) layouts based on the received functional integrated circuit design and one or more integrated circuit floorplans may be generated. One or more fuzzy logic rules may be applied to analyze attributes associated with each of the generated PnR layouts, and a PnR layout of the plurality of PnR layouts having an area utilization complying with the one or more fuzzy logic rules may be generated.