Incremental Placement Perturbation Prediction
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Solution Overview
Problem
Existing integrated circuit design methods face challenges in predicting and minimizing perturbation during incremental placement, leading to inferior quality of placement results and inefficiencies due to limitations in conventional flow-based techniques, simulated annealing, exhaustive search, diffusion-based placement, and clumping algorithms.
Innovation Solution
A method and system that predicts and minimizes perturbation by identifying initial placements, computing abstract flow, determining target locations, and using augmented clumping techniques and grid morphing to achieve legal placements with minimal disturbance, employing network flow and fluid propagation models for efficient component migration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional flow-based legalization techniques are used, then placement legalization is achieved, but perturbation prediction capability is lacking leading to inferior quality of placement results
Solution Approach 1:
The patent performs perturbation prediction before executing the actual placement legalization. By computing abstract flow and predicting cell movements in advance, the system can anticipate the impact of legalization operations and make informed decisions to minimize perturbation, rather than reacting after damage is done.
Solution Approach 2:
The patent implements a feedback mechanism where perturbation predictions are used to guide subsequent placement adjustments. The system continuously monitors predicted perturbation and adjusts legalization strategies accordingly, creating a closed-loop control system that optimizes placement quality iteratively.
2Manufacturing precision
If simulated annealing, exhaustive search, or diffusion-based placement techniques are used, then placement legalization is achieved, but computational efficiency is poor requiring more computer cycles
Solution Approach 1:
The patent divides the placement legalization problem into discrete cell-level operations and uses abstract flow computation to handle each cell independently rather than performing exhaustive global optimization. This segmentation enables linear-time complexity operations while maintaining legalization quality.
Solution Approach 2:
The patent changes the computational parameters from complex diffusion-based continuous optimization to discrete abstract flow computations with simplified movement predictions. This parameter transformation reduces computational complexity from exponential/diffusion-scale to linear time complexity while preserving essential legalization functionality.
3Productivity
If network flow or greedy techniques are used, then computational efficiency is improved, but perturbation reduction opportunities are missed due to arbitrary decisions
Solution Approach 1:
The patent incorporates perturbation prediction feedback into the network flow computation. Rather than making arbitrary greedy decisions, the system uses predicted perturbation values to guide flow assignments, ensuring that each computational decision is informed by its expected impact on placement quality.
Solution Approach 2:
The patent replaces traditional mechanical optimization approaches (exhaustive search, simulated annealing) with an abstract flow-based computational model. This substitution enables efficient computation while incorporating perturbation awareness through the fluid propagation model that predicts cell movements.
4Reliability
If incremental placement is performed with significant perturbation, then timing problems are corrected, but additional timing problems are introduced and convergence is hindered
Solution Approach 1:
The patent uses perturbation prediction as feedback to control the magnitude and direction of placement adjustments. By monitoring predicted perturbation levels, the system can adjust legalization strategies to correct timing issues while maintaining placement stability, preventing oscillations that would hinder convergence.
Solution Approach 2:
The patent performs preliminary perturbation analysis before executing placement corrections. This advance prediction allows the system to plan corrections that address timing problems while anticipating and avoiding the introduction of new timing issues, ensuring stable convergence toward the optimal solution.
Data Source
AI summary
Disclosed are a method, system, and computer program product for implementing incremental placement for an electronic design while predicting and minimizing a perturbation impact arising from incremental placement of electronic components. In some embodiments, an initial placement of an electronic design is identified, an abstract flow is computed, target locations of various electronic components to be placed are identified, a relative ordering of electronic components is determined, and the placement is then legalized. Furthermore, in various embodiments, the method, system, or computer program product starts with an initial placement of an electronic design and derives a legal placement by using an incremental placement technique while minimizing the perturbation impact or an total quadratic movement of instances. In some embodiments, an augmented or incremental clumping technique based data structure is utilized for rapid and substantially exact perturbation prediction of effects of local incremental placement operations.