A Comparative Study of Different Verification Platforms of AHB-SPI

This paper uses an Advanced High-performance Bus - Serial Peripheral Interface (AHB-SPI) module as the verification target and compares three verification platforms: Universal Verification Methodology/SystemVerilog (UVM/SV), the COroutine based COsimulation TestBench (cocotb), and UVM-Python. UVM/SV, the widely adopted method in the industry, offers a robust framework with modular and reusable components. However, it is constrained by its inherent nature as a hardware description language and deep learning curve of SV. Cocotb, based on Python, separates the simulation from the verification, mitigating some limitations associated with hardware description languages. Nevertheless, it lacks standardization and reusability, making it difficult to achieve uniformity. UVM-Python integrates the strengths of both approaches, offering a standardized yet flexible verification methodology. The study seeks to enhance the understanding of different verification platforms and provide guidance for verification engineers in selecting the most suitable methods for their projects.

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Publication Details

Published
2024-10-25
DOI
https://doi.org/10.1109/icicm63644.2024.10814204
Citations
1
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
0.25
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A Comparative Study of Different Verification Platforms of AHB-SPI

Yao Cheng, Guangtao Wang, Peiyong Zhang, Nianxiong Tan
1 citations
Brain Tumor Detection and Classification
0.25
article

A Comparative Study of Different Verification Platforms of AHB-SPI

Yao Cheng, Guangtao Wang, Peiyong Zhang, Nianxiong Tan
article en
1 citations

Abstract

This paper uses an Advanced High-performance Bus - Serial Peripheral Interface (AHB-SPI) module as the verification target and compares three verification platforms: Universal Verification Methodology/SystemVerilog (UVM/SV), the COroutine based COsimulation TestBench (cocotb), and UVM-Python. UVM/SV, the widely adopted method in the industry, offers a robust framework with modular and reusable components. However, it is constrained by its inherent nature as a hardware description language and deep learning curve of SV. Cocotb, based on Python, separates the simulation from the verification, mitigating some limitations associated with hardware description languages. Nevertheless, it lacks standardization and reusability, making it difficult to achieve uniformity. UVM-Python integrates the strengths of both approaches, offering a standardized yet flexible verification methodology. The study seeks to enhance the understanding of different verification platforms and provide guidance for verification engineers in selecting the most suitable methods for their projects.

Zhejiang University (CN)
Openalex Percentile: Top 40%
Brain Tumor Detection and Classification
0.25
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