GPU-Accelerated PCB Placement for Large-Scale Routing Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automated PCB placement tools struggle with large-scale designs, achieving limited placement quality and scalability due to the flexible design space and limited routing resources, with manual placement taking weeks and consuming 50% of the total design time.
Innovation Solution
A method and system using an iterative cost function to determine component placement on a PCB, incorporating wirelength, net crossing, and density parameters, optimized through gradient descent and Bayesian optimization, with GPU acceleration for improved scalability and design quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual placement methods are used, then placement quality can be maintained through expert judgment, but placement time increases significantly taking weeks to complete
Solution Approach 1:
The patent replaces manual mechanical placement operations with an automated computer-based system that uses cost function evaluation and optimization algorithms. The system automatically determines component placements by evaluating multiple candidate solutions against defined cost metrics (wirelength, net crossings, density) and selecting optimal placements, eliminating the need for manual expert judgment while significantly reducing placement time from weeks to minutes or hours.
Solution Approach 2:
The patent transforms the placement problem into a parameter optimization problem by defining a cost function with multiple parameters (wirelength, net crossings, density). The system varies placement parameters iteratively to minimize the cost function, transitioning from qualitative manual assessment to quantitative automated optimization. This parameter-based approach enables systematic exploration of the design space and automatic convergence to high-quality solutions.
2Productivity
If existing automated PCB placement tools are used, then placement time is reduced, but placement quality and scalability are limited due to the flexible design space and limited routing resources
Solution Approach 1:
The patent implements a dynamic optimization process where the cost function is evaluated iteratively across multiple candidate placements. The system dynamically adjusts placement decisions based on real-time cost function evaluations, considering interactions between multiple components and routing constraints. This dynamic approach allows the system to adapt to the flexible design space and complex routing resource constraints, achieving both high placement speed and quality.
Solution Approach 2:
The patent incorporates feedback mechanisms where the cost function evaluation results from each placement candidate inform subsequent placement decisions. The system uses the calculated cost metrics (wirelength, net crossings, density) as feedback to guide the optimization process, continuously improving placement quality by learning from previous evaluations. This feedback-driven approach enables the system to navigate the flexible design space effectively while maintaining high placement quality.
3Quantity of substance
If the number of components increases in large-scale designs, then design capability is enhanced, but routing resource constraints become more severe and placement complexity increases
Solution Approach 1:
The patent segments the complex placement problem into manageable components by defining a cost function with distinct terms for different placement objectives (wirelength, net crossings, density). Each term can be evaluated and optimized independently, allowing the system to handle large-scale designs with thousands of components by breaking down the overall complexity into smaller, tractable sub-problems that can be solved systematically.
Data Source
AI summary
A scalable, GPU-accelerated printed circuit board (PCB) placement process can utilize various cost factors to determine placement on the PCB. The cost factors can include the wirelength parameter, the net crossing parameter, the routability parameter, or the density parameter. Other factors can be utilized as well. The cost factor algorithm can be used to design a PCB placement using the desired optimizations. The processes can utilize a two-sided PCB, and rotation or orientation of each component design. The processes are scalability for large commercial designs maintaining the desired operating times and optimizations constraints. A synthesized benchmark suite to support tool comparisons and track progress is disclosed.