VLSI Placement Using Self-Supervised Graph Clustering for PPA
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Solution Overview
Problem
Conventional VLSI circuit placement techniques fail to optimize metrics such as routing congestion, timing degradation, and excessive power dissipation due to non-goal-directed graph neural network embeddings and lack of end-to-end differentiability in clustering cell placement.
Innovation Solution
A self-supervised graph clustering technique optimizes PPA metrics through goal-based loss functions and gradient backpropagation, using unsupervised training to learn embeddings and probability clustering, providing placement guidance that improves cell cluster assignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional feed-forward GNN embedding techniques are used for cell clustering, then the placement process is simple and fast, but the PPA metrics (routing congestion, timing, power) are not optimized
Solution Approach 1:
The patent implements feedback by formulating PPA metrics as differentiable loss functions that directly guide the GNN embedding generation. The loss function receives PPA metric values and gradients flow back through the embedding generation process, enabling the system to iteratively optimize embeddings to minimize routing congestion, timing degradation, and power dissipation while maintaining placement speed.
Solution Approach 2:
The patent changes the parameters of the GNN embedding generation process by making it goal-directed through PPA metric-based loss functions. Instead of generic feed-forward embedding, the system adjusts embedding parameters dynamically based on PPA metric optimization, transforming the embedding generation into a targeted process that directly improves routing congestion, timing, and power metrics.
2Loss of time
If conventional unsupervised clustering techniques are used, then the placement guidance is generated quickly, but the clustering does not optimize for specific PPA metrics
Solution Approach 1:
The system implements feedback by using PPA metric values to guide the clustering process through differentiable loss functions. The loss function receives PPA metric information and feeds it back into the embedding generation and clustering algorithms, enabling the system to quickly generate placement guidance that is specifically optimized for routing congestion, timing, and power metrics without requiring slow iterative optimization.
Solution Approach 2:
The patent replaces conventional mechanical clustering algorithms with a machine learning-based approach where PPA metrics are formulated as differentiable loss functions. This substitution allows the system to optimize clustering for specific metrics through gradient-based optimization rather than traditional iterative clustering methods, achieving both speed and metric optimization.
3Ease of manufacture
If non-differentiable clustering algorithms are used, then the implementation is straightforward, but end-to-end optimization for PPA metrics is not possible
Solution Approach 1:
The patent replaces non-differentiable mechanical clustering algorithms with a differentiable machine learning framework. PPA metrics are formulated as differentiable loss functions that can be optimized through gradient descent, enabling end-to-end optimization of the embedding generation and clustering process. This substitution maintains implementation simplicity while achieving sophisticated metric-optimized clustering.
Solution Approach 2:
The system changes the mathematical properties of the clustering algorithm by making the loss function differentiable with respect to the embedding parameters. This allows gradients to flow through the entire pipeline from PPA metrics to embedding generation, enabling end-to-end optimization while keeping the implementation straightforward through standard gradient-based optimization techniques.
Data Source
AI summary
A VLSI placement optimization framework receives a cell connectivity representation and cell characteristics and uses self-supervised graph clustering to optimize cell cluster assignments for power, performance, and area (PPA). The framework provides cell clustering constraints as placement guidance to commercial placers. Specifically, graph learning techniques are used to formulate the PPA metrics as machine learning loss functions that can be minimized directly through gradient descent. The framework improves the PPA metrics at the placement stage and the improvements endure to the post-route stage.