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Alberto Marchisio | Akateeminen Kirjakauppa

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Energy Efficiency and Robustness of Advanced Machine Learning Architectures - A Cross-Layer Approach
Alberto Marchisio; Muhammad Shafique
Taylor & Francis Ltd (2024)
Kovakantinen kirja
139,60
Tuotetta lisätty
ostoskoriin kpl
Siirry koriin
From Classical to Modern Algebraic Geometry : Corrado Segre's Mastership and Legacy
Gianfranco Casnati (ed.); Alberto Conte (ed.); Letterio Gatto (ed.); Livia Giacardi (ed.); Marina Marchisio (ed.); A Verra
Birkhäuser (2017)
Kovakantinen kirja
356,90
Tuotetta lisätty
ostoskoriin kpl
Siirry koriin
From Classical to Modern Algebraic Geometry - Corrado Segre's Mastership and Legacy
Gianfranco Casnati; Alberto Conte; Letterio Gatto; Livia Giacardi; Marina Marchisio; Alessandro Verra
Birkhauser Verlag AG (2018)
Pehmeäkantinen kirja
356,90
Tuotetta lisätty
ostoskoriin kpl
Siirry koriin
Energy Efficiency and Robustness of Advanced Machine Learning Architectures - A Cross-Layer Approach
139,60 €
Taylor & Francis Ltd
Sivumäärä: 347 sivua
Asu: Kovakantinen kirja
Julkaisuvuosi: 2024, 14.11.2024 (lisätietoa)
Kieli: Englanti
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 optimizing 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.

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Myymäläsaatavuus
Helsinki
Tapiola
Turku
Tampere
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ISBN:
9781032855509
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