Semantic Scholar Open Access 2023 6 sitasi

FSMx-Ultra: Finite State Machine Extraction From Gate-Level Netlist for Security Assessment

Rasheed Kibria Farimah Farahmandi M. Tehranipoor

Abstrak

Numerous security vulnerability assessment techniques urge precise and fast finite state machines (FSMs) extraction from the design under evaluation. Sequential logic locking, watermark insertion, fault-injection assessment of a system-on-a-chip (SoC) control flow, information leakage assessment, and reverse engineering at gate-level abstraction, to name a few, require precise FSM extraction from the synthesized netlist of the design. Unfortunately, no reliable solutions are currently available for fast and accurate extraction of FSMs from the highly unstructured gate-level netlist for effective security evaluation. The major challenge in developing such a solution is the precise recognition of FSM state flip-flops (FFs) in a netlist having a massive collection of FFs. In this article, we propose finite state machine extractor ultra (FSMx-Ultra), a framework for extracting FSMs from extremely unstructured gate-level netlists. FSMx-Ultra utilizes state-of-the-art graph theory concepts and algorithms to distinguish FSM state registers from other registers and then constructs gate-level state transition graphs (STGs) for each identified FSM state register using automatic test pattern generation (ATPG) techniques. The results of our experiments on 14 open-source benchmark designs illustrate that FSMx-Ultra can recover all FSMs quickly and precisely from synthesized gate-level netlists of diverse complexity and size utilizing various state encoding schemes.

Topik & Kata Kunci

Penulis (3)

R

Rasheed Kibria

F

Farimah Farahmandi

M

M. Tehranipoor

Format Sitasi

Kibria, R., Farahmandi, F., Tehranipoor, M. (2023). FSMx-Ultra: Finite State Machine Extraction From Gate-Level Netlist for Security Assessment. https://doi.org/10.1109/TCAD.2023.3266368

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Informasi Jurnal
Tahun Terbit
2023
Bahasa
en
Total Sitasi
Sumber Database
Semantic Scholar
DOI
10.1109/TCAD.2023.3266368
Akses
Open Access ✓