Proceedings of ISP RAS

A Survey of Methods for Model Extraction from HDL Descriptions

S. Smolov (ISP RAS, Moscow)


In this paper a survey of existing methods of model extraction from hardware system descriptions written in Hardware Description Languages (like Verilog and VHDL) is presented. There are many tasks in hardware and software design where models are applied. The most actual tasks that are mentioned in this paper are: code optimization, logical synthesis optimization, model abstraction, and functional verification. The model categories that are mostly described here are flow or dependency graph models and automata models. As for flow graphs or dependency graphs, the methods of program slices extraction are described in details. Program slices can be characterized as suitable enough for directed test generation. Almost all the described automata models are finite state machine models and extended finite state machine models and so methods of such models extraction are the most popular for logical synthesis optimization and for functional test generation. The tests that can be generated from automata models shows high coverage of the target description. The key problems of existing model extraction methods are: the complexity of an application to industrial hardware descriptions (because of their complex structure), lack of automation (sometimes the hardware designer’s knowledge is needed), the absence of open-source implementations. Also it is an actual task to create extendible frameworks for integration of different model extraction and analysis methods. Such framework can help in development of effective hybrid methods for hardware synthesis and verification.


hardware description languages; model extraction; static analysis; code optimization; model abstraction; logical synthesis; functional verification; program slicing; flow graphs; dependency graphs; finite state machines; extended finite state machines


Proceedings of the Institute for System Programming, vol. 27, issue 1, 2015, pp. 97-124.

ISSN 2220-6426 (Online), ISSN 2079-8156 (Print).

DOI: 10.15514/ISPRAS-2015-27(1)-6

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