9781032870137 - energy efficiency and robustness of advanced machine learning architectures: a cross-layer approach (chapman & hall/crc artificial intelligence and robotics series) de marchisio, alberto; shafique, muhammad (12 resultados)
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2026
Serie: Libro 39 de 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
- Tapa blanda
Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 74,81
Envío por EUR 7,58Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 3 disponibles
Condición: New.
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2026
Serie: Libro 39 de 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
- Tapa blanda
Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 84,42
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New.
Idioma: Inglés
Editorial: CRC Press, 2026
Serie: Libro 39 de 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
- Tapa blanda
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de AmericaPBShop.store US
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 87,35
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Idioma: Inglés
Editorial: CRC Press, 2026
Serie: Libro 39 de 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
- Tapa blanda
Librería: PBShop.store UK, Fairford, GLOS, Reino UnidoPBShop.store UK
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 82,88
Envío por EUR 5,85Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2026
Serie: Libro 39 de 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
- Tapa blanda
Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
Contactar con el vendedorVendedor de 4 estrellasCondición: Nuevo
EUR 88,83
Envío por EUR 9,95Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 3 disponibles
Condición: New.
Idioma: Inglés
Editorial: Taylor & Francis Ltd, 2026
Serie: Libro 39 de 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
- Tapa blanda
Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 104,87
Envío por EUR 14,57Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Paperback. Condición: Brand New. 360 pages. 7.01x0.82x10.00 inches. In Stock.
Idioma: Inglés
Editorial: CRC Press, 2026
Serie: Libro 39 de 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
- Tapa blanda
Librería: moluna, Greven, Alemaniamoluna
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 81,53
Envío por EUR 48,99Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. Alberto Marchisio received his B.Sc. and M.Sc. degrees in Electronic Engineering from Politecnico di Torino, Turin, Italy, in October 2015 and April 2018, respectively. He received his Ph.D. degree in Computer Science from the Technische Universit.
Energy Efficiency and Robustness of Advanced Machine Learning Architectures : A Cross-Layer Approach
Idioma: Inglés
Editorial: Taylor & Francis Ltd Jun 2026, 2026
Serie: Libro 39 de 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
- Tapa blanda
Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 109,00
Envío por EUR 30,50Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 2 disponibles
Taschenbuch. Condición: Neu. Neuware.
Idioma: Inglés
Editorial: Taylor & Francis Ltd, 2026
Serie: Libro 39 de 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
- Tapa blanda
- Impresión bajo demanda
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de AmericaGrand Eagle Retail
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 82,63
Gastos de envío gratisSe envía dentro de Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy-efficiency, robustness, and techniques that typically focus on optim…izing for a single objective/have a limited set of goals.This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. More specifically, this book improves the energy efficiency of complex models like CapsNets, through a specialized flow of hardware-level designs and software-level optimizations exploiting the application-driven knowledge of these systems and the error tolerance through approximations and quantization. This book also improves the robustness of ML models, in particular for SNNs executed on neuromorphic hardware, due to their inherent cost-effective features. This book integrates multiple optimization objectives into specialized frameworks for jointly optimizing the robustness and energy efficiency of these systems.This is an important resource for students and researchers of computer and electrical engineering who are interested in developing energy efficient and robust ML.The Open Access version of this book, available at , has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license. This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Idioma: Inglés
Editorial: Chapman and Hall/CRC, 2026
Serie: Libro 39 de 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
- Tapa blanda
- Impresión bajo demanda
Librería: THE SAINT BOOKSTORE, Southport, Reino UnidoTHE SAINT BOOKSTORE
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 90,19
Envío por EUR 18,67Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: Más de 20 disponibles
Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Idioma: Inglés
Editorial: Taylor & Francis Ltd, 2026
Serie: Libro 39 de 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
- Tapa blanda
- Impresión bajo demanda
Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 96,45
Envío por EUR 31,92Se envía de Australia a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy-efficiency, robustness, and techniques that typically focus on optim…izing for a single objective/have a limited set of goals.This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. More specifically, this book improves the energy efficiency of complex models like CapsNets, through a specialized flow of hardware-level designs and software-level optimizations exploiting the application-driven knowledge of these systems and the error tolerance through approximations and quantization. This book also improves the robustness of ML models, in particular for SNNs executed on neuromorphic hardware, due to their inherent cost-effective features. This book integrates multiple optimization objectives into specialized frameworks for jointly optimizing the robustness and energy efficiency of these systems.This is an important resource for students and researchers of computer and electrical engineering who are interested in developing energy efficient and robust ML.The Open Access version of this book, available at , has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license. This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Idioma: Inglés
Editorial: Taylor & Francis Ltd, 2026
Serie: Libro 39 de 48 - Chapman & Hall/CRC Artificial Intelligence and Robotics
- Tapa blanda
- Impresión bajo demanda
Librería: CitiRetail, Stevenage, Reino UnidoCitiRetail
Contactar con el vendedorVendedor de 5 estrellasCondición: Nuevo
EUR 88,85
Envío por EUR 43,14Se envía de Reino Unido a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Paperback. Condición: new. Paperback. Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy-efficiency, robustness, and techniques that typically focus on optim…izing for a single objective/have a limited set of goals.This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. More specifically, this book improves the energy efficiency of complex models like CapsNets, through a specialized flow of hardware-level designs and software-level optimizations exploiting the application-driven knowledge of these systems and the error tolerance through approximations and quantization. This book also improves the robustness of ML models, in particular for SNNs executed on neuromorphic hardware, due to their inherent cost-effective features. This book integrates multiple optimization objectives into specialized frameworks for jointly optimizing the robustness and energy efficiency of these systems.This is an important resource for students and researchers of computer and electrical engineering who are interested in developing energy efficient and robust ML.The Open Access version of this book, available at , has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license. This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
