Enhancing Electronic Design Automation Tools with an ML-Based Information Retrieval System

Vikash Kumar; Shideh Yavary Mehr

doi:doi:10.11648/j.ijiis.20241303.12

Research Article |

| Peer-Reviewed

Enhancing Electronic Design Automation Tools with an ML-Based Information Retrieval System

Vikash Kumar

, Shideh Yavary Mehr^*

Published in International Journal of Intelligent Information Systems (Volume 13, Issue 3)

Received: 17 May 2024 Accepted: 4 June 2024 Published: 19 June 2024

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Abstract

Over the past fifty years, Electronic Design Automation (EDA) tools have played a crucial role in the semiconductor industry, assisting in the design, simulation, and manufacturing of integrated circuits (ICs). However, the sophisticated nature of these tools often demands extensive expertise, which can be a barrier for many users. Mastery of these tools necessitates specialized knowledge and skills, including comprehension of complex algorithms, design methodologies, and tool-specific workflows. To address this challenge, this paper introduces a machine learning (ML) based information retrieval system designed to enhance the usability of EDA tools. The objective of this system is to simplify user interactions and make EDA tools more accessible to designers, regardless of their expertise level. The main idea of this ML-driven system is to provide a chatbot-like interface that facilitates efficient, context-aware searches and offers interactive, step-by-step guidance on using various tool functionalities. By integrating natural language processing and machine learning techniques, the system can understand user queries, extract relevant information from the tool's documentation, and provide context-specific guidance. This approach helps to mitigate the steep learning curve associated with advanced EDA applications and enhances tool accessibility. Consequently, it promotes a more intuitive interaction with sophisticated EDA software, thus fostering enhanced usability of complex tools in the semiconductor industry. This work exemplifies the transformative potential of integrating machine learning with conversational user interfaces in making sophisticated software applications more user-friendly.

Published in	International Journal of Intelligent Information Systems (Volume 13, Issue 3)
DOI	10.11648/j.ijiis.20241303.12
Page(s)	53-58
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Machine Learning, Electronic Design Automation, Natural Language Processing, Information Retrieval, Semantic Search Technology Integration

References

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[2]	“What Is Electronic Design Automation (EDA)? – How It Works.” Synopsys, www.synopsys.com/glossary/what-is-electronic-design-automation.html Accessed 15 May 2024.
[3]	Mrbullwinkle. “Azure Openai Service Embeddings Tutorial - Azure Openai.” Azure OpenAI Service Embeddings Tuto-rial - Azure OpenAI \| Microsoft Learn, learn.microsoft.com/en-us/azure/ai-services/openai/tutorials/embeddings?tabs=python-new%2Ccommand-line&pivots=programming-language-python Accessed 15 May 2024.
[4]	What Is Electronic Design Automation (EDA)? \| Cadence, www.cadence.com/en_US/home/explore/what-is-electronic-design-automation.html Accessed 15 May 2024.
[5]	Alto, Valentina. Modern Generative AI with ChatGPT and OpenAI Models: Leverage the capabilities of OpenAI's LLM for productivity and innovation with GPT3 and GPT4. Packt Publishing Ltd, 2023.
[6]	MacMillen, D., et al. “An industrial view of Electronic Design Automation.” IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 19, no. 12, 2000, pp. 1428–1448, https://doi.org/10.1109/43.898825.
[7]	Mhlanga, D. (2023). Open AI in Education, the Responsible and Ethical Use of ChatGPT Towards Lifelong Learning. In: FinTech and Artificial Intelligence for Sustainable Development. Sustainable Development Goals Series. Palgrave Macmillan, Cham.doi: https://doi.org/10.1007/9
[8]	Zhou, Joyce, and Thorsten Joachims. "GPT as a Baseline for Recommendation Explanation Texts." arXiv preprint arXiv: 2309.08817 (2023). https://doi.org/10.48550/arXiv.2309.08817
[9]	Silva, Ítallo, et al. “Leveraging CHATGPT for automated human-centered explanations in recommender systems.” Proceedings of the 29th International Conference on Intelligent User Interfaces, 18 Mar. 2024, https://doi.org/10.1145/3640543.3645171.
[10]	Božić, Velibor. "Application of artificial intelligence in user interface design." (2023).
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[12]	Soliev Bakhromjon Nabijonovich, & Gʻiyosiddinov Najmiddin. (2024). OPTIMIZING PYQT5 DEVELOPMENT WITH QT DESIGNER. Web of Teachers: Inderscience Research, 2(4), 254–259. Retrieved from https://webofjournals.com/index.php/1/article/view/1224Fdsfdf
[13]	Azzuni, Hussam, et al. “Utalk: Bridging the gap between humans and ai.” 2024 IEEE International Conference on Consumer Electronics (ICCE), 6 Jan. 2024, https://doi.org/10.1109/icce59016.2024.10444441
[14]	Guha, R., et al. “Semantic search.” Proceedings of the Twelfth International Conference on World Wide Web - WWW ’03, 2003, https://doi.org/10.1145/775152.775250
[15]	Raza, Muhammad Raheel, et al. “Sentiment analysis using Deep Learning in Cloud.” 2021 9th International Symposium on Digital Forensics and Security (ISDFS), 28 June 2021, https://doi.org/10.1109/isdfs52919.2021.9486312

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APA Style

Kumar, V., Mehr, S. Y. (2024). Enhancing Electronic Design Automation Tools with an ML-Based Information Retrieval System. International Journal of Intelligent Information Systems, 13(3), 53-58. https://doi.org/10.11648/j.ijiis.20241303.12

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ACS Style

Kumar, V.; Mehr, S. Y. Enhancing Electronic Design Automation Tools with an ML-Based Information Retrieval System. Int. J. Intell. Inf. Syst. 2024, 13(3), 53-58. doi: 10.11648/j.ijiis.20241303.12

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AMA Style

Kumar V, Mehr SY. Enhancing Electronic Design Automation Tools with an ML-Based Information Retrieval System. Int J Intell Inf Syst. 2024;13(3):53-58. doi: 10.11648/j.ijiis.20241303.12

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@article{10.11648/j.ijiis.20241303.12,
  author = {Vikash Kumar and Shideh Yavary Mehr},
  title = {Enhancing Electronic Design Automation Tools with an ML-Based Information Retrieval System
},
  journal = {International Journal of Intelligent Information Systems},
  volume = {13},
  number = {3},
  pages = {53-58},
  doi = {10.11648/j.ijiis.20241303.12},
  url = {https://doi.org/10.11648/j.ijiis.20241303.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20241303.12},
  abstract = {Over the past fifty years, Electronic Design Automation (EDA) tools have played a crucial role in the semiconductor industry, assisting in the design, simulation, and manufacturing of integrated circuits (ICs). However, the sophisticated nature of these tools often demands extensive expertise, which can be a barrier for many users. Mastery of these tools necessitates specialized knowledge and skills, including comprehension of complex algorithms, design methodologies, and tool-specific workflows. To address this challenge, this paper introduces a machine learning (ML) based information retrieval system designed to enhance the usability of EDA tools. The objective of this system is to simplify user interactions and make EDA tools more accessible to designers, regardless of their expertise level. The main idea of this ML-driven system is to provide a chatbot-like interface that facilitates efficient, context-aware searches and offers interactive, step-by-step guidance on using various tool functionalities. By integrating natural language processing and machine learning techniques, the system can understand user queries, extract relevant information from the tool's documentation, and provide context-specific guidance. This approach helps to mitigate the steep learning curve associated with advanced EDA applications and enhances tool accessibility. Consequently, it promotes a more intuitive interaction with sophisticated EDA software, thus fostering enhanced usability of complex tools in the semiconductor industry. This work exemplifies the transformative potential of integrating machine learning with conversational user interfaces in making sophisticated software applications more user-friendly.
},
 year = {2024}
}

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TY  - JOUR
T1  - Enhancing Electronic Design Automation Tools with an ML-Based Information Retrieval System

AU  - Vikash Kumar
AU  - Shideh Yavary Mehr
Y1  - 2024/06/19
PY  - 2024
N1  - https://doi.org/10.11648/j.ijiis.20241303.12
DO  - 10.11648/j.ijiis.20241303.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 53
EP  - 58
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20241303.12
AB  - Over the past fifty years, Electronic Design Automation (EDA) tools have played a crucial role in the semiconductor industry, assisting in the design, simulation, and manufacturing of integrated circuits (ICs). However, the sophisticated nature of these tools often demands extensive expertise, which can be a barrier for many users. Mastery of these tools necessitates specialized knowledge and skills, including comprehension of complex algorithms, design methodologies, and tool-specific workflows. To address this challenge, this paper introduces a machine learning (ML) based information retrieval system designed to enhance the usability of EDA tools. The objective of this system is to simplify user interactions and make EDA tools more accessible to designers, regardless of their expertise level. The main idea of this ML-driven system is to provide a chatbot-like interface that facilitates efficient, context-aware searches and offers interactive, step-by-step guidance on using various tool functionalities. By integrating natural language processing and machine learning techniques, the system can understand user queries, extract relevant information from the tool's documentation, and provide context-specific guidance. This approach helps to mitigate the steep learning curve associated with advanced EDA applications and enhances tool accessibility. Consequently, it promotes a more intuitive interaction with sophisticated EDA software, thus fostering enhanced usability of complex tools in the semiconductor industry. This work exemplifies the transformative potential of integrating machine learning with conversational user interfaces in making sophisticated software applications more user-friendly.

VL  - 13
IS  - 3
ER  -

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Author Information

Vikash Kumar

Department of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, The United States

Contact Email

http://orcid.org/0009-0007-3980-9199
Shideh Yavary Mehr

Department of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, The United States

Contact Email

http://orcid.org/0009-0009-1646-8496

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Plain Text BibTeX RIS

APA Style

Kumar, V., Mehr, S. Y. (2024). Enhancing Electronic Design Automation Tools with an ML-Based Information Retrieval System. International Journal of Intelligent Information Systems, 13(3), 53-58. https://doi.org/10.11648/j.ijiis.20241303.12

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ACS Style

Kumar, V.; Mehr, S. Y. Enhancing Electronic Design Automation Tools with an ML-Based Information Retrieval System. Int. J. Intell. Inf. Syst. 2024, 13(3), 53-58. doi: 10.11648/j.ijiis.20241303.12

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AMA Style

Kumar V, Mehr SY. Enhancing Electronic Design Automation Tools with an ML-Based Information Retrieval System. Int J Intell Inf Syst. 2024;13(3):53-58. doi: 10.11648/j.ijiis.20241303.12

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@article{10.11648/j.ijiis.20241303.12,
  author = {Vikash Kumar and Shideh Yavary Mehr},
  title = {Enhancing Electronic Design Automation Tools with an ML-Based Information Retrieval System
},
  journal = {International Journal of Intelligent Information Systems},
  volume = {13},
  number = {3},
  pages = {53-58},
  doi = {10.11648/j.ijiis.20241303.12},
  url = {https://doi.org/10.11648/j.ijiis.20241303.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20241303.12},
  abstract = {Over the past fifty years, Electronic Design Automation (EDA) tools have played a crucial role in the semiconductor industry, assisting in the design, simulation, and manufacturing of integrated circuits (ICs). However, the sophisticated nature of these tools often demands extensive expertise, which can be a barrier for many users. Mastery of these tools necessitates specialized knowledge and skills, including comprehension of complex algorithms, design methodologies, and tool-specific workflows. To address this challenge, this paper introduces a machine learning (ML) based information retrieval system designed to enhance the usability of EDA tools. The objective of this system is to simplify user interactions and make EDA tools more accessible to designers, regardless of their expertise level. The main idea of this ML-driven system is to provide a chatbot-like interface that facilitates efficient, context-aware searches and offers interactive, step-by-step guidance on using various tool functionalities. By integrating natural language processing and machine learning techniques, the system can understand user queries, extract relevant information from the tool's documentation, and provide context-specific guidance. This approach helps to mitigate the steep learning curve associated with advanced EDA applications and enhances tool accessibility. Consequently, it promotes a more intuitive interaction with sophisticated EDA software, thus fostering enhanced usability of complex tools in the semiconductor industry. This work exemplifies the transformative potential of integrating machine learning with conversational user interfaces in making sophisticated software applications more user-friendly.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Enhancing Electronic Design Automation Tools with an ML-Based Information Retrieval System

AU  - Vikash Kumar
AU  - Shideh Yavary Mehr
Y1  - 2024/06/19
PY  - 2024
N1  - https://doi.org/10.11648/j.ijiis.20241303.12
DO  - 10.11648/j.ijiis.20241303.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 53
EP  - 58
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20241303.12
AB  - Over the past fifty years, Electronic Design Automation (EDA) tools have played a crucial role in the semiconductor industry, assisting in the design, simulation, and manufacturing of integrated circuits (ICs). However, the sophisticated nature of these tools often demands extensive expertise, which can be a barrier for many users. Mastery of these tools necessitates specialized knowledge and skills, including comprehension of complex algorithms, design methodologies, and tool-specific workflows. To address this challenge, this paper introduces a machine learning (ML) based information retrieval system designed to enhance the usability of EDA tools. The objective of this system is to simplify user interactions and make EDA tools more accessible to designers, regardless of their expertise level. The main idea of this ML-driven system is to provide a chatbot-like interface that facilitates efficient, context-aware searches and offers interactive, step-by-step guidance on using various tool functionalities. By integrating natural language processing and machine learning techniques, the system can understand user queries, extract relevant information from the tool's documentation, and provide context-specific guidance. This approach helps to mitigate the steep learning curve associated with advanced EDA applications and enhances tool accessibility. Consequently, it promotes a more intuitive interaction with sophisticated EDA software, thus fostering enhanced usability of complex tools in the semiconductor industry. This work exemplifies the transformative potential of integrating machine learning with conversational user interfaces in making sophisticated software applications more user-friendly.

VL  - 13
IS  - 3
ER  -

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