Graph Neural Network Embeddings for IC Netlist Design
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Solution Overview
Problem
The process of testing integrated circuit designs is resource-intensive and prone to redundancy, particularly as the number of test cases increases, leading to inefficiencies in computational resources and prolonged processing times.
Innovation Solution
The use of machine learning techniques to generate effective graph-based representations of electronic circuits, allowing for the prediction of performance, power, and area metrics, as well as code equivalency checks, by creating invariant functional embeddings and applying contrastive learning to extract logically equivalent functional entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of test cases is increased to verify integrated circuit performance, then measurement precision and reliability are improved, but resource consumption and processing time increase significantly
Solution Approach 1:
The patent creates functional copies of circuit subgraphs through graph neural network embeddings. These embeddings capture the essential functional characteristics of circuit components without requiring exhaustive physical testing of every test case, thereby reducing processing time while maintaining measurement precision.
Solution Approach 2:
The patent performs preliminary analysis by generating graph embeddings and identifying functionally equivalent subgraphs before executing full test cases. This preliminary action filters out redundant tests and focuses computational resources on critical paths, reducing overall processing time while maintaining comprehensive coverage.
2Measurement precision
If the number of test cases is increased to cover more logic paths, then measurement precision is improved, but resource utilization increases
Solution Approach 1:
The patent uses graph neural network embeddings to create compressed functional representations of circuit subgraphs. These embeddings serve as proxies that capture essential functional information without requiring exhaustive testing of all logic paths, thereby reducing computational resource utilization while maintaining measurement precision.
Solution Approach 2:
The patent applies partial action by focusing testing efforts on critical subgraphs identified through functional equivalence analysis. Rather than exhaustively testing all possible logic paths, the system identifies and tests only the most significant portions of the circuit, reducing resource utilization while maintaining adequate coverage.
3Productivity
If functional equivalence checking is performed on circuit subgraphs, then productivity is improved by reducing redundant tests, but device complexity increases due to machine learning model requirements
Solution Approach 1:
The patent creates functional copies of circuit subgraphs through graph neural network embeddings. These embeddings enable automated functional equivalence checking that identifies redundant test cases, improving productivity by eliminating duplicate testing efforts despite the added complexity of the machine learning model.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for evaluating electronic circuit designs. A directed graph representing a netlist design for an electrical circuit is accessed, the netlist design comprising a plurality of electronic components and a plurality of connections among the plurality of electronic components. A node in the directed graph is selected, the node corresponding to a register that receives input from one or more of the plurality of electronic components in the netlist design. A subgraph is generated for the node, based on the directed graph, comprising identifying a connectivity cone ending at the first register. A functional embedding is generated for the subgraph based on a trained encoder machine learning model. A predicted performance characteristic of the netlist design is generated based at least in part on the functional embedding.