Explainable Artificial Intelligence for Better Design of Very Large Scale Integrated Circuits PDF Download
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Author: Wei Zeng
Publisher:
ISBN:
Category :
Languages : en
Pages : 95
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Book Description
With the advance of Very Large Scale Integration (VLSI) technology, the design process of VLSI circuits becomes more complex, challenging, and time-consuming. Recent years have seen a rising trend of machine learning (ML) incorporated in VLSI design flow for better and more efficient design and implementation of integrated circuits. Explainable Artificial Intelligence (XAI) is an emerging technique that aims to perform prediction tasks while providing explanations for the predictions. XAI adds transparency and trustworthiness to ML models, leading to better human understanding and exploitation of the models. With ML being applied in VLSI design, it is desirable to adopt ideas from XAI for even better and more trustworthy outcomes of VLSI design. This dissertation explores the usage of Shapley Additive Explanation (SHAP)--a recent development in XAI, on different aspects and stages of VLSI design flow. Specifically, we propose three techniques that adopt SHAP in front-end and back-end design flows, including (a) SHAP-guided layout obfuscation for enhanced hardware security in split manufacturing, (b) explainable routability prediction, which accelerates the physical design flow and provides hints for improving the design, and (c) explainable-ML-guided approximate logic synthesis for area-efficient computing in error-tolerant applications. These are the first works that incorporate XAI into VLSI design methodology. All of them achieve better results than their conventional counterparts or existing works in similar settings.
Author: Wei Zeng
Publisher:
ISBN:
Category :
Languages : en
Pages : 95
Get Book Here
Book Description
With the advance of Very Large Scale Integration (VLSI) technology, the design process of VLSI circuits becomes more complex, challenging, and time-consuming. Recent years have seen a rising trend of machine learning (ML) incorporated in VLSI design flow for better and more efficient design and implementation of integrated circuits. Explainable Artificial Intelligence (XAI) is an emerging technique that aims to perform prediction tasks while providing explanations for the predictions. XAI adds transparency and trustworthiness to ML models, leading to better human understanding and exploitation of the models. With ML being applied in VLSI design, it is desirable to adopt ideas from XAI for even better and more trustworthy outcomes of VLSI design. This dissertation explores the usage of Shapley Additive Explanation (SHAP)--a recent development in XAI, on different aspects and stages of VLSI design flow. Specifically, we propose three techniques that adopt SHAP in front-end and back-end design flows, including (a) SHAP-guided layout obfuscation for enhanced hardware security in split manufacturing, (b) explainable routability prediction, which accelerates the physical design flow and provides hints for improving the design, and (c) explainable-ML-guided approximate logic synthesis for area-efficient computing in error-tolerant applications. These are the first works that incorporate XAI into VLSI design methodology. All of them achieve better results than their conventional counterparts or existing works in similar settings.
Author: Ibrahim (Abe) M. Elfadel
Publisher: Springer
ISBN: 9783030046651
Category : Technology & Engineering
Languages : en
Pages : 694
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Book Description
This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center
Author: Balwinder Raj
Publisher: CRC Press
ISBN: 1000901505
Category : Computers
Languages : en
Pages : 261
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Book Description
Artificial intelligence (AI), machine learning, and advanced electronic circuits involve learning from every data input and using those inputs to generate new rules for future business analytics. AI and machine learning are now giving us new opportunities to use big data that we already had, as well as unleash a whole lot of new use cases with new data types. With the increasing use of AI dealing with highly sensitive information such as healthcare, adequate security measures are required to securely store and transmit this information. This book provides a broader coverage of the basic aspects of advanced circuits design and applications. AI for Big Data-Based Engineering Applications from Security Perspectives is an integrated source that aims at understanding the basic concepts associated with the security of advanced circuits. The content includes theoretical frameworks and recent empirical findings in the field to understand the associated principles, key challenges, and recent real-time applications of advanced circuits, AI, and big data security. It illustrates the notions, models, and terminologies that are widely used in the area of Very Large Scale Integration (VLSI) circuits, security, identifies the existing security issues in the field, and evaluates the underlying factors that influence system security. This work emphasizes the idea of understanding the motivation behind advanced circuit design to establish the AI interface and to mitigate security attacks in a better way for big data. This book also outlines exciting areas of future research where already existing methodologies can be implemented. This material is suitable for students, researchers, and professionals with research interest in AI for big data–based engineering applications, faculty members across universities, and software developers.
Author: Anthony J. Gadient
Publisher:
ISBN:
Category : Integrated circuits
Languages : en
Pages : 12
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Book Description
We achieve this support by using meta-design information and machine learning techniques to incrementally characterize the design space and learn how each synthesis tool moves a design around that design space. The result is a self-improving design environment with a tool control capability that allows the designer to more efficiently produce an implementation that satisfies area and performance constraints."
Author: Vazgen Melikyan
Publisher: Springer Nature
ISBN: 3031507142
Category : Technology & Engineering
Languages : en
Pages : 351
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Book Description
This book describes machine learning-based new principles, methods of design and optimization of high-speed integrated circuits, included in one electronic system, which can exchange information between each other up to 128/256/512 Gbps speed. The efficiency of methods has been proven and is described on the examples of practical designs. This will enable readers to use them in similar electronic system designs. The author demonstrates newly developed principles and methods to accelerate communication between ICs, working in non-standard operating conditions, considering signal deviation compensation with linearity self-calibration. The observed circuit types also include but are not limited to mixed-signal, high performance heterogeneous integrated circuits as well as digital cores.
Author: Carnegie-Mellon University. SRC-CMU Research Center for Computer-Aided Design
Publisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages : 24
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Book Description
Abstract: "As computer chips have become increasingly, complex, there has been an ever increasing need for better computer-aided design (CAD) tools to assist the designer. This need has brought forth a wealth of computer programs which can aid in design and has also demonstrated the need for more powerful programming paradigms. Artificial intelligence (AI) is considered to be one such paradigm that can help to design a new generation of more powerful computer tools. This paper reviews the progress of AI for the design of integrated circuits and analyzes nine case studies in an effort to determine the role AI should play in CAD for VLSI chips."
Author: Kok-Hoo Yeap
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Patrick DeWilde
Publisher: Springer
ISBN: 9781461288336
Category : Technology & Engineering
Languages : en
Pages : 220
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Book Description
A modern microelectronic circuit can be compared to a large construction, a large city, on a very small area. A memory chip, a DRAM, may have up to 64 million bit locations on a surface of a few square centimeters. Each new generation of integrated circuit- generations are measured by factors of four in overall complexity -requires a substantial increase in density from the current technology, added precision, a decrease of the size of geometric features, and an increase in the total usable surface. The microelectronic industry has set the trend. Ultra large funds have been invested in the construction of new plants to produce the ultra large-scale circuits with utmost precision under the most severe conditions. The decrease in feature size to submicrons -0.7 micron is quickly becoming availabl- does not only bring technological problems. New design problems arise as well. The elements from which microelectronic circuits are build, transistors and interconnects, have different shape and behave differently than before. Phenomena that could be neglected in a four micron technology, such as the non-uniformity of the doping profile in a transistor, or the mutual capacitance between two wires, now play an important role in circuit design. This situation does not make the life of the electronic designer easier: he has to take many more parasitic effects into account, up to the point that his ideal design will not function as originally planned.
Author: Alberto Sangiovanni-Vincentelli
Publisher: IEEE
ISBN: 9780879422271
Category : Technology & Engineering
Languages : en
Pages : 163
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Book Description
Author: Wojciech Samek
Publisher: Springer Nature
ISBN: 3030289540
Category : Computers
Languages : en
Pages : 435
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Book Description
The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.
