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.
Book describes online experimentation, using fundamentally emergent technologies to build the resources and considering the context of IoT.Online Experimentation: Emerging Technologies and IoT is suitable for all who is involved in the development design and building of the domain of remote experiments.
Este libro es una clara muestra de que la historia, para ser verdaderamente humana, no puede consistir en un mero conjunto de datos económicos y políticos. En efecto, por sus páginas desfilan cuarenta y seis sacerdotes que, a menudo con medios muy pobres, cambiaron el mundo a su alrededor como fundadores, teólogos, predicadores, simples curas de parroquia, mártires, misioneros, profesores o santos. Los sacerdotes seleccionados se han dividido en siete grupos: maestros del espíritu, misioneros de pueblos lejanos, perseguidos a causa de la justicia, grandes teólogos, sacerdotes que se anticiparon a su tiempo, apóstoles de la caridad y los dedicados a diversos apostolados. No todos los sacerdotes descritos son santos (aunque muchos estén en camino de ser reconocidos como tales), pero todos dejaron sin duda una profunda huella en el siglo XX.
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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