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.
Keywords
Machine Learning, Electronic Design Automation, Natural Language Processing, Information Retrieval, Semantic Search Technology Integration
1. Introduction
In the rapidly evolving semiconductor industry, where time-to-market and design complexity are paramount, the efficiency and usability of EDA tools are fundamental to ensuring successful and timely product development. However, the sheer breadth of documentation and intricate functionalities associated with these tools often pose formidable challenges, resulting in extended learning curves and productivity bottlenecks. The extensive knowledge required to navigate and master these sophisticated tools can be daunting, even for experienced designers, hindering their ability to leverage the full potential of these powerful design environments. As semiconductor devices become more sophisticated to enhance performance, minimize power consumption, and achieve greater integration, navigating through EDA tools becomes increasingly daunting for those aiming to enter or excel in the field. The proposed ML-driven information retrieval and guidance system aims to address these challenges by providing a natural language interface that allows users to interact with the EDA tools conversationally, like interacting with a virtual assistant or chatbot. This system would leverage state-of-the-art natural language processing (NLP) and machine learning techniques to understand the user's queries, extract relevant information from the tool's documentation, and provide context-specific guidance in a human-readable format
| [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. |
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Furthermore, the proposed system can learn from user interactions and adapt its responses over time, continuously improving its understanding of the tool's functionalities and the users' preferences. This adaptive learning capability ensures that the system remains relevant and effective as the tools evolve and new features are introduced.
In addition to providing context-specific guidance, the system can also offer step-by-step instructions for various tool operations, effectively acting as a virtual tutor or mentor. This feature can be particularly valuable for new users, guiding them through complex tasks and accelerating their learning process.
By seamlessly integrating this ML-driven system into EDA tools, designers can focus on their core responsibilities without being bogged down by the complexities of navigating vast documentation or struggling with tool-specific nuances. This approach not only enhances productivity and efficiency but also fosters a more intuitive and user-friendly experience
| [10] | Božić, Velibor. "Application of artificial intelligence in user interface design." (2023). |
[10]
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In summary, the integration of an ML-driven information retrieval and guidance system into EDA tools has the potential to revolutionize the user experience, streamline the design process, and drive innovation in the semiconductor industry. By combining the power of natural language processing, machine learning, and domain-specific knowledge, this approach can bridge the gap between the complexity of EDA tools and the need for efficient and user-friendly design environments
.
To effectively follow this paper, readers should have a basic understanding of concepts related to machine learning, data extraction, and chat completion. Familiarity with EDA tools and their usage would be helpful. Understanding the capabilities and potential applications of OpenAI Services would also be advantageous, as this paper utilizes similar technologies to enhance user interactions within EDA tools. The purpose of this research is to propose a machine learning-driven information retrieval and guidance system for EDA tools, aiming to streamline user interactions and enhance usability. Its significance lies in bridging the gap between the complexity of EDA tools and the need for efficient and user-friendly design environments, potentially revolutionizing the semiconductor industry's design processes.
2. Background Study and Related Works
This section discusses the background and previous work related to two key areas: Electronic Design Automation (EDA) tools and chatbots.
2.1. Electronic Design Automation (EDA) Tool
The history of EDA can be traced back to the 1960s when large, vertically integrated original equipment manufacturers (OEMs) developed internal chip design and manufacturing capabilities. Commercial EDA tools emerged in three phases: the first phase saw the availability of computer-assisted interactive graphics design systems (CAD/CAM), the second phase witnessed the birth of the application-specific integrated circuit (ASIC) industry and the rise of simulation, design, and verification tools (CAE), and the third phase saw the emergence of broad-line EDA suppliers automating larger portions of the IC design process. Today, the EDA industry continues to evolve, driven by the dramatic expansion of semiconductor technology and the need for more comprehensive tools
.
The Electronic Design Automation (EDA) tools are essential for the design and development of integrated circuits (ICs) and electronic systems. These tools have undergone significant evolution over time, spanning different phases and addressing various aspects of the design process. This section provides an overview of the three main categories of EDA tools and their historical development
.
2.1.1. Simulation
Simulation tools are used to predict the behavior of a proposed circuit before its physical implementation. These tools take a description of the circuit, typically written in a hardware description language like Verilog or Very High-Speed Integrated Circuit (VHDL) and model the behavior of its components at varying levels of detail. The level of detail required is determined by the type of circuit and its intended application. Simulation tools perform various operations to forecast the resulting behavior of the circuit
.
2.1.2. Design
Design tools assist in assembling the collection of circuit elements that implement the desired circuit functionality. In this process, called logic synthesis, suitable circuit components are chosen and connected in a way that makes sense to accomplish the desired function. Alternatively, it can be physical, where the geometric shapes that represent the circuit in silicon are assembled, placed, and routed together (known as place and route). Additionally, custom layout tools allow for an interactive design process guided by the designer
.
2.1.3. Verification
Verification tools examine the logical or physical representation of the chip to ensure its correctness and performance. Physical verification checks if the interconnected geometries comply with the manufacturing requirements of the fabrication facility. Functional verification compares the implemented circuit with the original specification to ensure it faithfully represents the required function (e.g., layout vs. schematic (LVS)). Simulation-based verification techniques compare the actual behavior with the expected behavior using input stimuli. Formal verification approaches, such as equivalence checking, verify the behavior algorithmically without the need for input stimuli
.
2.2. Chatbot
Chatbots are software programs designed to converse with humans through natural language interactions.
2.2.1. Early Beginnings
The concept of chatbots and the Turing Test gained traction in the 1950s, thanks to Alan Turing's work. ELIZA, created in 1966, is considered the first known chatbot, using pattern matching and template-based responses
| [1] | Betrand, Chidi Ukamaka, et al. “Intelligent learning system using interactive dialogflow and Webhooks.” International Journal of Intelligent Information Systems, 26 Dec. 2023, https://doi.org/10.11648/j.ijiis.20231204.11 |
[1]
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2.2.2. Advancements in Chatbot Technology
PARRY (1972) and ALICE (1995, won Loebner Prize) improved upon early chatbots' capabilities. Recent progress in AI, machine learning, and natural language processing enabled more sophisticated chatbots. Chatbots have found applications in customer service, education, healthcare, and information services. They have the potential to transform various industries with their conversational abilities
| [1] | Betrand, Chidi Ukamaka, et al. “Intelligent learning system using interactive dialogflow and Webhooks.” International Journal of Intelligent Information Systems, 26 Dec. 2023, https://doi.org/10.11648/j.ijiis.20231204.11 |
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2.2.3. Categorization of Chatbots
1) Menu/button-based: Use decision trees and top-down menus for user input.
2) Keyword recognition-based: Identify specific keywords to provide relevant responses.
3) Contextual chatbots: Leverage machine learning, intent recognition, and context for more natural conversations
| [1] | Betrand, Chidi Ukamaka, et al. “Intelligent learning system using interactive dialogflow and Webhooks.” International Journal of Intelligent Information Systems, 26 Dec. 2023, https://doi.org/10.11648/j.ijiis.20231204.11 |
[1]
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3. Enhancing Electronic Design Automation Tools with an ML-Based Information Retrieval System
The enhancement of the existing Electronic Design Automation (EDA) tool involved the integration of a machine learning-powered query system designed to aid users in navigating its complex features more efficiently. This enhancement was achieved through a two-phase process:
3.1. Developing a Retrieval System for Step-by-Step Task Guidance
This initial phase focused on creating a robust retrieval system capable of providing users with detailed, step-by-step guidance for performing tasks within the EDA tool. The implementation involved several intricate steps:
3.1.1. Data Preparation
The data specific to the EDA tool, including user manuals, support documents, and typical user queries, was collected as raw data. This data underwent a cleansing process, which was crucial for preparing the data for machine learning processing. The specific steps involved in this cleansing process were:
1) HTML Tag Removal: All HTML tags were stripped from the data to ensure that only textual content was processed, eliminating any web formatting elements that could skew data interpretation.
2) Numeric Character Removal: Numeric characters were removed from the text to focus on linguistic data and to maintain consistency across text inputs.
3) Case Normalization: The entire dataset was converted to lowercase to standardize the data and reduce complexity for the machine learning models.
4) Whitespace Correction: Excess whitespace within the data was eliminated, including spaces at the beginning and end of strings, as well as multiple spaces reduced to a single space.
5) Punctuation Removal: All punctuation marks were removed, simplifying the text, and avoiding any influence of punctuation on the data processing.
6) Stop Word Removal: Commonly used words (stop words) that offer minimal value in understanding user intents, such as "and", "the", or "of", were removed from the text.
7) Stemming: Words were reduced to their base or root form, streamlining the dataset, and ensuring that variations of the same word were processed uniformly.
3.1.2. Vector Embedding Generation
Vector embeddings represent text data in a numerical vector. format, where each word or phrase is mapped to a dense vector of real numbers capturing semantic meaning and context. In this implementation, the "text-embedding-ada-002" model from Azure OpenAI is used to generate vector embeddings for cleaned data from the EDA-specific information. The model took each chunk of text as input and produced a corresponding vector representation, where semantically similar text chunks would have similar vector embeddings encoding contextual relationships, word associations, and semantic similarities present in the original data. These vector embeddings, capturing the semantic essence of the EDA-specific information, were systematically stored in a local database, creating a rich repository of vectorized data. This repository enables efficient search, comparison, and analysis of the EDA-specific information based on the semantic similarities captured by the vector embeddings, rather than relying solely on the raw text data. The advantage of using vector embeddings lies in their ability to facilitate efficient similarity calculations and comparisons between text data points, proving valuable for information retrieval, and semantic search
.
3.1.3. Query Processing and Matching
When a user submits a query, it is transformed into a vector embedding utilizing the Azure OpenAI model ("text-embedding-ada-002"). The system then employs the cosine similarity metric a method that calculates the cosine of the angle between two vectors in a multi-dimensional space, ranging from 1 (identical), with 0 indicating no similarity to perform a comparison between the user’s query vector and the vectors stored in the database. This comparison identifies the vectors that are most semantically similar to the query. Vectors with the highest cosine similarity scores are considered the best matches, and the corresponding data is subsequently retrieved for response generation. This method is pivotal in pinpointing the most relevant responses quickly and accurately
.
Figure 1. Enhanced EDA Doc Assistant.
3.1.4. Response Generation
The system utilizes GPT-3.5 Turbo to generate responses based on the retrieved data, which serves as input for the language model. This state-of-the-art model excels in producing coherent, contextually appropriate responses that guide users through their queries within the EDA tool. Crucial to the process is prompt engineering, which involves carefully crafting prompts to ensure that the responses are not only relevant but also precise and helpful, thereby enhancing the user's interaction with the EDA tool. The generated responses leverage the model's capabilities to provide human-like, chatbot-style assistance, seamlessly guiding users through their data exploration tasks
| [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. |
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3.2. Integration of the Retrieval System into the Existing EDA Tool
This phase focused on embedding the newly developed retrieval system seamlessly into the existing Qt-based desktop application of the EDA tool. It comprised:
Figure 2. Embedded retrieval system.
The
figure 2 illustrates the process of integrating the information retrieval system into the EDA tool, where users input their queries and receive step-by-step guidance to execute actions effectively
.
3.2.1. User Interface
A dedicated chat bot like pane is added to the user interface, acting as the main interaction point for users to submit queries and receive guidance.
3.2.2. Backend Integration
Once the UI components were in place, the next step was to integrate the retrieval system's backend functionality into the existing infrastructure of the EDA tool. This involved establishing communication channels between the UI components and the backend servers responsible for processing user queries, retrieving relevant data, and generating responses.
Figure 3. EDA and Doc Assistant.
By incorporating this advanced, machine learning-powered retrieval system into the EDA tool, users are now equipped with a powerful assistant that simplifies complex tasks, enhancing productivity and user experience within a familiar tool environment.
4. Future Work
The successful implementation of a machine learning-enhanced information retrieval system in Electronic Design Automation (EDA) tools marks a significant advancement in the usability and functionality of these complex systems. However, this is only the beginning of what can be achieved with the integration of advanced technologies in EDA tools. The future work will focus on further enhancing the interaction between users and the tool, automating more aspects of the design process, and improving the overall system intelligence. This could include:
4.1. Automation of Design Steps
One of the most promising areas for future development is the automation of design steps. Currently, our system provides users with interactive, step-by- step instructions based on their queries. To extend this functionality, I propose the development of a feature that allows users to opt-in for the automatic execution of these suggested design steps. This could include.
4.2. Intelligent Task Automation
This point focuses on utilizing machine learning models not just to suggest design steps but also to automate their execution with user approval or predefined preferences. By integrating machine learning algorithms into the design process, the system can learn from user interactions and patterns, thereby enhancing its ability to automate tasks efficiently. For example, if a user consistently approves certain design suggestions, the system can learn to automate those steps without requiring explicit confirmation each time. This approach minimizes manual input, streamlines the design process, and reduces the likelihood of human error.
4.3. Context-Aware Adaptations
Enhancing the system to recognize the context of the design task and adapt its suggestions and automations accordingly. This would ensure that the automated actions are always aligned with the current project goals and user needs.
4.4. Enhanced User Control and Customization:
Providing users with greater control over the automation process, allowing them to customize the level of automation according to their preferences and requirements. Users should have the flexibility to define conditions under which the tool should automate actions and specify exceptions where manual intervention is preferred. For example, users may want certain critical design decisions to be made manually, while allowing routine tasks to be automated. By offering such customization options, the system accommodates diverse user needs and workflows, empowering users to strike the right balance between automation and manual control based on their individual preferences and project dynamics
| [11] | Ali, Sarah, and Magda Fayek. “A state graph-based improved framework for Monkey GUI testing for EDA Desktop applications.” 2023 13th International Conference on Software Technology and Engineering (ICSTE), 27 Oct. 2023, https://doi.org/10.1109/icste61649.2023.00010. |
[11]
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The proposed enhancements aim to advance the automation of design steps by leveraging machine learning, enhancing contextual awareness, and providing users with greater control and customization options. This approach promises to streamline the design process, improve efficiency, and ultimately deliver better outcomes for users.
5. Conclusions
The integration of a machine learning-powered retrieval system into the existing Electronic Design Automation (EDA) tool significantly enhances its usability, efficiency, and productivity. The system simplifies navigation and accelerates task completion, reducing the learning curve and minimizing user frustration. This smart assistant feature not only speeds up the design process but also improves the quality of outputs, allowing users to focus more on creative and critical aspects of electronic design. As a result, the EDA tool evolves into a more user-friendly, efficient, and productive platform, setting a new standard for interactive, intelligent software tools in electronic design.
Author Contributions
Vikash Kumar: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft
Shideh Yavary Mehr: Conceptualization, Investigation, Methodology, Supervision, Validation
Conflicts of Interest
The authors declare no conflicts of interest.
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Cite This Article
-
-
@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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