This paper discusses Python SystemVerilog (Python SV), a simulation-based verification approach leveraging the power of Python and SystemVerilog. The use of Python-implemented UVM classes in SystemVerilog en...This paper discusses Python SystemVerilog (Python SV), a simulation-based verification approach leveraging the power of Python and SystemVerilog. The use of Python-implemented UVM classes in SystemVerilog enables users to write less code, minimize errors and reduce the verification time. This paper evaluates the use of Python SV in the verification of digital designs, its benefits, limitations, and future prospects. Python-SystemVerilog (Python-SV) is a research area that investigates the feasibility of building a high-level verification environment using Python and SystemVerilog. Python-SV aims to provide a unified framework for the design, simulation, and verification of digital systems, with an emphasis on ease of use and productivity. SystemVerilog is a hardware description and verification language that is widely used for designing digital systems. On the other hand, Python is a powerful, high-level programming language that is widely used in various fields, including software engineering, scientific computing, and data analysis. Python’s popularity has grown in recent years, primarily due to its simplicity, ease of use, and wide range of libraries and frameworks. Python-SV research primarily focuses on the following areas: 1) Integration of Python and SystemVerilog: Python-SV aims to seamlessly integrate SystemVerilog and Python, allowing designers to write test benches and verification code in Python and interface them with SystemVerilog modules. This integration simplifies the development process, making it easier to write and maintain large and complex verification environments. 2) Development of Python libraries for verification: Python-SV research focuses on developing Python libraries specifically for digital system verification. These libraries provide a higher-level interface for writing test benches and other functions, such as analysis and visualization of simulation results. 3) Implementation of verification methodologies: Python-SV research investigates the implementation of various industry-standard verification methodologies, such as the Universal Verification Methodology (UVM), in Python. This implementation aims to enable designers to use Python to develop and simulate UVM-compliant test benches. 4) Development of simulation tools: Python-SV also explores the development of simulation tools that extend the capabilities of traditional SystemVerilog simulators. These tools leverage the capabilities of Python for complex data analysis and visualization and provide a more intuitive and user-friendly interface for working with simulation results. Overall, Python-SV research aims to bring the benefits of Python to the world of digital system verification, enabling designers to build more efficient, productive, and flexible verification environments.展开更多
Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes...Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes the performance gains from parallel versus sequential hyperparameter optimization. Using scikit-learn’s Randomized SearchCV, this project tuned a Random Forest classifier for fake news detection via randomized grid search. Setting n_jobs to -1 enabled full parallelization across CPU cores. Results show the parallel implementation achieved over 5× faster CPU times and 3× faster total run times compared to sequential tuning. However, test accuracy slightly dropped from 99.26% sequentially to 99.15% with parallelism, indicating a trade-off between evaluation efficiency and model performance. Still, the significant computational gains allow more extensive hyperparameter exploration within reasonable timeframes, outweighing the small accuracy decrease. Further analysis could better quantify this trade-off across different models, tuning techniques, tasks, and hardware.展开更多
文摘This paper discusses Python SystemVerilog (Python SV), a simulation-based verification approach leveraging the power of Python and SystemVerilog. The use of Python-implemented UVM classes in SystemVerilog enables users to write less code, minimize errors and reduce the verification time. This paper evaluates the use of Python SV in the verification of digital designs, its benefits, limitations, and future prospects. Python-SystemVerilog (Python-SV) is a research area that investigates the feasibility of building a high-level verification environment using Python and SystemVerilog. Python-SV aims to provide a unified framework for the design, simulation, and verification of digital systems, with an emphasis on ease of use and productivity. SystemVerilog is a hardware description and verification language that is widely used for designing digital systems. On the other hand, Python is a powerful, high-level programming language that is widely used in various fields, including software engineering, scientific computing, and data analysis. Python’s popularity has grown in recent years, primarily due to its simplicity, ease of use, and wide range of libraries and frameworks. Python-SV research primarily focuses on the following areas: 1) Integration of Python and SystemVerilog: Python-SV aims to seamlessly integrate SystemVerilog and Python, allowing designers to write test benches and verification code in Python and interface them with SystemVerilog modules. This integration simplifies the development process, making it easier to write and maintain large and complex verification environments. 2) Development of Python libraries for verification: Python-SV research focuses on developing Python libraries specifically for digital system verification. These libraries provide a higher-level interface for writing test benches and other functions, such as analysis and visualization of simulation results. 3) Implementation of verification methodologies: Python-SV research investigates the implementation of various industry-standard verification methodologies, such as the Universal Verification Methodology (UVM), in Python. This implementation aims to enable designers to use Python to develop and simulate UVM-compliant test benches. 4) Development of simulation tools: Python-SV also explores the development of simulation tools that extend the capabilities of traditional SystemVerilog simulators. These tools leverage the capabilities of Python for complex data analysis and visualization and provide a more intuitive and user-friendly interface for working with simulation results. Overall, Python-SV research aims to bring the benefits of Python to the world of digital system verification, enabling designers to build more efficient, productive, and flexible verification environments.
文摘Hyperparameter tuning is a key step in developing high-performing machine learning models, but searching large hyperparameter spaces requires extensive computation using standard sequential methods. This work analyzes the performance gains from parallel versus sequential hyperparameter optimization. Using scikit-learn’s Randomized SearchCV, this project tuned a Random Forest classifier for fake news detection via randomized grid search. Setting n_jobs to -1 enabled full parallelization across CPU cores. Results show the parallel implementation achieved over 5× faster CPU times and 3× faster total run times compared to sequential tuning. However, test accuracy slightly dropped from 99.26% sequentially to 99.15% with parallelism, indicating a trade-off between evaluation efficiency and model performance. Still, the significant computational gains allow more extensive hyperparameter exploration within reasonable timeframes, outweighing the small accuracy decrease. Further analysis could better quantify this trade-off across different models, tuning techniques, tasks, and hardware.
