Jyoti Kandpal – författare

The text provides a comprehensive and forward-looking exploration of the integration between low-power CMOS VLSI design and cutting-edge technologies such as artificial intelligence and machine learning.

Explores how artificial intelligence techniques are revolutionizing VLSI design, focusing on optimizing low-power circuits and systems. Presents the latest advancements and research in both artificial intelligence and VLSI design, such as FinFET and nanosheet-based logic, analog integrated circuits design, and optimization with artificial intelligence. Offers perspectives on future trends and potential developments in the integration of artificial intelligence with VLSI design to reduce the complexity and time-consuming process of semiconductor IC design. Addresses techniques for achieving energy efficiency in both digital and analog components and presents future trends in low-power VLSI design. Discusses topics such as artificial intelligence-driven adaptive power management in VLSI design and neural networks in low-power VLSI architectures.

The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, microelectronics, VLSI design, artificial intelligence, computer science and engineering.

The text provides a comprehensive and forward-looking exploration of the integration between low-power CMOS VLSI design and cutting-edge technologies such as artificial intelligence and machine learning.

Explores how artificial intelligence techniques are revolutionizing VLSI design, focusing on optimizing low-power circuits and systems. Presents the latest advancements and research in both artificial intelligence and VLSI design, such as FinFET and nanosheet-based logic, analog integrated circuits design, and optimization with artificial intelligence. Offers perspectives on future trends and potential developments in the integration of artificial intelligence with VLSI design to reduce the complexity and time-consuming process of semiconductor IC design. Addresses techniques for achieving energy efficiency in both digital and analog components and presents future trends in low-power VLSI design. Discusses topics such as artificial intelligence-driven adaptive power management in VLSI design and neural networks in low-power VLSI architectures.

The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, microelectronics, VLSI design, artificial intelligence, computer science and engineering.

The text provides a comprehensive and forward-looking exploration of the integration between low-power CMOS VLSI design and cutting-edge technologies such as artificial intelligence and machine learning.Explores how artificial intelligence techniques are revolutionizing VLSI design, focusing on optimizing low-power circuits and systems.Presents the latest advancements and research in both artificial intelligence and VLSI design, such as FinFET and nanosheet-based logic, analog integrated circuits design, and optimization with artificial intelligence.Offers perspectives on future trends and potential developments in the integration of artificial intelligence with VLSI design to reduce the complexity and time-consuming process of semiconductor IC design.Addresses techniques for achieving energy efficiency in both digital and analog components and presents future trends in low-power VLSI design.Discusses topics such as artificial intelligence-driven adaptive power management in VLSI design and neural networks in low-power VLSI architectures.The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, microelectronics, VLSI design, artificial intelligence, computer science and engineering.

Unlock the future of nanotechnology with this essential guide, which provides an exhaustive exploration of solutions to overcome the physical limits of silicon and optimize the performance of next-generation nanoscale semiconductor devices. As the role of microelectronics grows in today’s world, there is a need for new material solutions that move away from the limited conductivity of silicon to improve the electronic properties in nanoscale semiconductor devices. This unique guide exhaustively explores a number of topics, including 2D materials, microelectronic devices, large VLSI circuit design, and leakage mitigation techniques. The book showcases in-depth analyses of microelectronic device structures and their applications, properties, and characterization. With its easy-to-understand approach, this book is an excellent resource for novices and experts alike.

Unlock the future of nanotechnology with this essential guide, which provides an exhaustive exploration of solutions to overcome the physical limits of silicon and optimize the performance of next-generation nanoscale semiconductor devices. As the role of microelectronics grows in today s world, there is a need for new material solutions that move away from the limited conductivity of silicon to improve the electronic properties in nanoscale semiconductor devices. This unique guide exhaustively explores a number of topics, including 2D materials, microelectronic devices, large VLSI circuit design, and leakage mitigation techniques. The book showcases in-depth analyses of microelectronic device structures and their applications, properties, and characterization. With its easy-to-understand approach, this book is an excellent resource for novices and experts alike.

A practical guide to the use of FPGAs for deep learning and its real-world applications in signal processing In Deep Learning Meets FPGA, a team of distinguished researchers delivers an expert discussion on how to use field programmable gate arrays (FPGAs) to apply deep learning techniques to signal processing. The book explains why technologists may decide to forego the traditional methods of using CPU and GPU architectures so they can access the improved processing speed, flexibility, and efficiency of FPGA technology. The book discusses FPGA architecture, optimization techniques, toolchains, and frameworks for FPGA development. It covers the implementation of convolutional neural networks, recurrent neural networks, and real-time processing applications. The information is accompanied by example use cases in audio and video signal processing, as well as strategies for power-efficient FPGA designs. Readers will also find: A thorough introduction to the challenges and obstacles posed by traditional approaches to deep learning applications in signal processing and how those can be solved using FPGAsComprehensive explorations of deep learning applications in sensor data integrationPractical discussions of up-to-date debugging and validation techniques using FPGA designsCutting-edge explorations of potential future trends and promising areas of research for further development of FPGAsPerfect for computer science researchers and postgraduate students interested in signal processing, Deep Learning Meets FPGA will also benefit practicing signal processing engineers.