IC Netlist Graph Standardization for RTL Recovery
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Solution Overview
Problem
Existing methods lack an efficient way to recover the Register Transfer Level (RTL) representation of an integrated circuit (IC) from a netlist, which is crucial for re-fabricating obsolete ICs or establishing hardware assurance.
Innovation Solution
A method involving a non-transitory storage medium with instructions to convert a netlist into a graph, standardize the graph, and then generate an RTL representation using hardware description language (HDL) constructs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the RTL representation is converted to a gate level netlist for secure IC fabrication, then security against reverse engineering is improved, but the ability to recover functional behavior for legitimate purposes (re-fabrication, verification) deteriorates
Solution Approach 1:
The patent introduces an intermediary system that sits between the secure netlist and the desired RTL representation. This system uses machine learning models trained on pairs of netlists and their corresponding RTL representations to predict and recover the functional behavior without actually reversing the compilation process. The intermediary preserves security while enabling legitimate recovery purposes.
Solution Approach 2:
Instead of attempting to directly reverse engineer the original RTL code, the system creates a functional copy or approximation of the behavior using machine learning predictions. The learned model generates RTL-like representations that capture the functional essence without requiring exact reconstruction of the source code, thus enabling re-fabrication and verification while maintaining security.
2Reliability
If advanced verification and validation techniques are applied to establish hardware trust, then hardware assurance is improved, but the complexity of the analysis process increases
Solution Approach 1:
The system performs preliminary analysis by training machine learning models on representative datasets before actual verification tasks. Once trained, the models can quickly predict functional behavior and identify potential issues without requiring complex real-time analysis. This preliminary training phase simplifies subsequent verification processes while maintaining high assurance levels.
Solution Approach 2:
The patent replaces traditional mechanical or manual analysis methods with machine learning-based automated analysis. Instead of using complex computational algorithms and manual verification steps, the system uses trained neural networks to predict functional behavior, thereby reducing analysis complexity while improving reliability through consistent automated evaluation.
Data Source
AI summary
A Register Transfer Level (RTL) representation is recovered from a netlist representing an integrated circuit (IC). The netlist is converted to a graph comprising nodes belonging to a set of node types and edges connecting the nodes. The set of node types includes an instance node type representing an electronic component and a wire node type representing signal transfer between components. The graph is converted to a standardized graph by replacing subgraphs of the graph with standardized subgraphs. An RTL representation of the standardized graph is generated by operations including building signal declarations in a hardware description language (HDL) from the wire nodes of the standardized graph and building signal assignments in the HDL from instance nodes of the standardized graph.