Graph Neural Network Cell Placement Optimization
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Solution Overview
Problem
Existing electronic design automation (EDA) tools face challenges in achieving optimal power, performance, and area (PPA) thresholds during cell placement in semiconductor manufacturing, particularly in large-scale designs, often relying on user inputs that trade off performance for power/area or failing to work effectively in modern technology nodes.
Innovation Solution
An automated approach using a neural network/machine learning model guides cell placement by analyzing cell placement arrangements to improve PPA, employing a Graph Neural Network (GNN) to learn embeddings for nodes in a gate-level netlist, and converting these embeddings into soft placement guides for EDA tools to optimize cell positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If automated machine learning approaches are used to guide cell placement, then manufacturing precision and power-performance-area optimization improve, but device complexity and computational requirements increase
Solution Approach 1:
A machine learning model serves as an intermediary between design specifications and cell placement decisions. The model learns optimal placement strategies from training data and automatically generates placement recommendations, eliminating the need for manual expert intervention while achieving superior PPA optimization results
Solution Approach 2:
The machine learning model is trained in advance on large datasets of design patterns and their corresponding optimal placements. This preliminary training phase enables the model to automatically apply learned knowledge to new designs without requiring real-time complex computations during the actual placement process
2Productivity
If automated machine learning approaches are used to guide cell placement, then productivity and optimization quality improve, but ease of operation deteriorate due to increased automation complexity
Solution Approach 1:
The machine learning model operates autonomously to guide cell placement decisions. It automatically analyzes design inputs, generates placement recommendations, and iterates on optimization without requiring manual user inputs or adjustments, enabling the system to self-manage the complex optimization process
Solution Approach 2:
The system incorporates feedback mechanisms where placement results are evaluated against power, performance, and area metrics. This feedback loop allows the machine learning model to continuously improve placement quality by learning from actual outcomes, automatically adjusting strategies to achieve better PPA thresholds
Data Source
AI summary
Systems, apparatus, articles of manufacture, and methods are disclosed to improve cell placement in semiconductor dies. An apparatus includes interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to extract a gate-level netlist from a cell placement arrangement, the cell placement arrangement corresponding to cells on a semiconductor die, extract information from the gate-level netlist corresponding to an operation of the gate-level netlist, use unsupervised learning to learn an embedding for a node in the gate-level netlist, and update the cell placement arrangement based on the learned embedding of the node.