[PDF] Explainable Artificial Intelligence For Better Design Of Very Large Scale Integrated Circuits eBook

Author : Wei Zeng
Publisher :
Page : 95 pages
File Size : 27,30 MB
Release : 2021
Category :
ISBN :

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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
Page : 694 pages
File Size : 28,77 MB
Release : 2019-03-16
Category : Technology & Engineering
ISBN : 9783030046651

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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
Page : 261 pages
File Size : 27,74 MB
Release : 2023-06-30
Category : Computers
ISBN : 1000901505

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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 : Vazgen Melikyan
Publisher : Springer Nature
Page : 351 pages
File Size : 43,28 MB
Release : 2024-01-31
Category : Technology & Engineering
ISBN : 3031507142

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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 : Alex James
Publisher : CRC Press
Page : 250 pages
File Size : 10,38 MB
Release : 2023-04-03
Category : Technology & Engineering
ISBN : 1000873757

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Memristors have provided a new direction of thinking for circuit designers to overcome the limits of scalability and for thinking of building systems beyond Moore’s law. Over the last decade, there has been a significant number of innovations in using memristors for building neural networks through analog computing, in-memory computing, and stochastic computing approaches. The emergence of intelligent integrated circuits is inevitable for the future of integrated circuit applications. This book provides a collection of talks conducted as part of the IEEE Seasonal School on Circuits and System, having a focus on Intelligence in Chip: Tomorrow of Integrated Circuits. Technical topics discussed in the book include: Edge of Chaos Theory Explains Complex Phenomena in Memristor Circuits Analog Memristive Computing Designing energy efficient neo-cortex system with on-device learning Integrated sensors Challenges and recent advances in NVM based Neuromorphic Computing ICs In-memory Computing (for deep learning) Deep learning with Spiking Neural Networks Computational Intelligence for Designing Integrated Circuits and Systems Neurochip Design, Modeling, and Applications

Author : Mayuri Mehta
Publisher : Springer Nature
Page : 273 pages
File Size : 46,63 MB
Release : 2022-10-19
Category : Technology & Engineering
ISBN : 3031128079

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This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas. The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.

Author : João P. S. Rosa
Publisher : Springer Nature
Page : 117 pages
File Size : 35,71 MB
Release : 2019-12-11
Category : Technology & Engineering
ISBN : 3030357430

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This book addresses the automatic sizing and layout of analog integrated circuits (ICs) using deep learning (DL) and artificial neural networks (ANN). It explores an innovative approach to automatic circuit sizing where ANNs learn patterns from previously optimized design solutions. In opposition to classical optimization-based sizing strategies, where computational intelligence techniques are used to iterate over the map from devices’ sizes to circuits’ performances provided by design equations or circuit simulations, ANNs are shown to be capable of solving analog IC sizing as a direct map from specifications to the devices’ sizes. Two separate ANN architectures are proposed: a Regression-only model and a Classification and Regression model. The goal of the Regression-only model is to learn design patterns from the studied circuits, using circuit’s performances as input features and devices’ sizes as target outputs. This model can size a circuit given its specifications for a single topology. The Classification and Regression model has the same capabilities of the previous model, but it can also select the most appropriate circuit topology and its respective sizing given the target specification. The proposed methodology was implemented and tested on two analog circuit topologies.

Author : Vladik Kreinovich
Publisher : Springer Nature
Page : 136 pages
File Size : 25,13 MB
Release : 2022-09-16
Category : Technology & Engineering
ISBN : 3031099745

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Modern AI techniques –- especially deep learning –- provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. It is therefore desirable to provide natural-language explanation of the numerical AI recommendations. The need to connect natural language rules and numerical decisions is known since 1960s, when the need emerged to incorporate expert knowledge -- described by imprecise words like "small" -- into control and decision making. For this incorporation, a special "fuzzy" technique was invented, that led to many successful applications. This book described how this technique can help to make AI more explainable.The book can be recommended for students, researchers, and practitioners interested in explainable AI.

Author : Ovidiu Vermesan
Publisher : CRC Press
Page : 143 pages
File Size : 32,57 MB
Release : 2023-05-05
Category : Computers
ISBN : 1000881911

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Recent technological developments in sensors, edge computing, connectivity, and artificial intelligence (AI) technologies have accelerated the integration of data analysis based on embedded AI capabilities into resource-constrained, energy-efficient hardware devices for processing information at the network edge. Embedded AI combines embedded machine learning (ML) and deep learning (DL) based on neural networks (NN) architectures such as convolutional NN (CNN), or spiking neural network (SNN) and algorithms on edge devices and implements edge computing capabilities that enable data processing and analysis without optimised connectivity and integration, allowing users to access data from various sources. Embedded AI efficiently implements edge computing and AI processes on resource-constrained devices to mitigate downtime and service latency, and it successfully merges AI processes as a pivotal component in edge computing and embedded system devices. Embedded AI also enables users to reduce costs, communication, and processing time by assembling data and by supporting user requirements without the need for continuous interaction with physical locations. This book provides an overview of the latest research results and activities in industrial embedded AI technologies and applications, based on close cooperation between three large-scale ECSEL JU projects, AI4DI, ANDANTE, and TEMPO. The book’s content targets researchers, designers, developers, academics, post-graduate students and practitioners seeking recent research on embedded AI. It combines the latest developments in embedded AI, addressing methodologies, tools, and techniques to offer insight into technological trends and their use across different industries.

Author : Carnegie-Mellon University. SRC-CMU Research Center for Computer-Aided Design
Publisher :
Page : 24 pages
File Size : 33,71 MB
Release : 1987
Category : Artificial intelligence
ISBN :

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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."