conference-paper

UVM Components for the Secure Hash 512 Algorithm

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The Secure Hash Algorithm (SHA-512), which offers data integrity and security, is a well-liked cryptographic hash function. As algorithms' complexity increases, it becomes increasingly crucial to ensure that they are correct and covered within the verification process. This work presents a detailed methodology for certifying the SHA-512 algorithm using the Universal Verification Methodology (UVM). Complex designs may be efficiently verified using UVM's standardised architecture for building extendable and reusable verification environments. The SHA-512 algorithm is divided into modular components that each handle a specific function as the initial stage in the verification process. UVM component-based verification technique is used for the modelling and verification of each individual component. The UVM component architecture promotes modular design while allowing for independent verification and reuse.A functional coverage model captures the key traits, behaviours, and unique situations of the SHA-512 algorithm. The UVM testbench is linked with the coverage model to track the development and effectiveness of the verification efforts. Coverage-driven verification techniques are employed to guarantee thorough evaluation of key algorithmic components. A number of UVM analysis tools are also used to extensively evaluate the SHA-512 algorithm. These analysis components acquire and review data during the simulation, providing insight into the algorithm's behaviour and aiding in the identification of any issues or irregularities. The proposed UVM-based verification approach may be used to efficiently and systematically verify the SHA-512 algorithm. UVM components are appropriate for complex algorithms like SHA-512 verification due to their adaptability, reuse, and scalability. Functional coverage and analytical elements are combined to ensure. The validation of the UVM components performed by the functional coverage and the UVM environment covered 83.33% of the possible outcomes.

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Publication details

DOI
10.1109/icccnt56998.2023.10308037
OpenAlex
W4388936872
Document type
conference-paper
Language
EN
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