|
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910484854803321 |
|
|
Autore |
Galindez Olascoaga Laura Isabel |
|
|
Titolo |
Hardware-aware probabilistic machine learning models : learning, inference and use cases / / Laura Isabel Galindez Olascoaga, Wannes Meetr, Marian Verhelst |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Cham, Switzerland : , : Springer, , [2021] |
|
©2021 |
|
|
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Edizione |
[1st ed. 2021.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (XII, 163 p. 51 illus.) |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di bibliografia |
|
Includes bibliographical references and index. |
|
|
|
|
|
|
Nota di contenuto |
|
Introduction -- Background -- Hardware-Aware Cost Models -- Hardware-Aware Bayesian Networks for Sensor Front-End Quality Scaling -- Hardware-Aware Probabilistic Circuits -- Run-Time Strategies -- Conclusions. |
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performance of the machine learning task, with the overarching goal of balancing the two optimally. The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover. The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show |
|
|
|
|
|
|
|
|
|
|
|
|
|
the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering. Introduces a new, systematic approach for the realization of hardware-awareness with probabilistic models; Enables readers to accommodate various systems and applications, as demonstrated with multiple use cases targeting distinct types of devices; Describes novel methods to deal with some of the challenges of extreme-edge computing, a paradigm that has recently garnered attention as a complementary approach to cloud computing; Represents one of the first efforts systematically to bring probabilistic inference to the world of edge computing, by means of novel algorithmic insights and strategies. . |
|
|
|
|
|
|
2. |
Record Nr. |
UNICAMPANIAVAN0268764 |
|
|
Autore |
Grove, Larry C. |
|
|
Titolo |
Finite reflection groups / L. C. Grove, C. T. Benson |
|
|
|
|
|
Pubbl/distr/stampa |
|
|
New York, : Springer, 1985 |
|
|
|
|
|
|
|
Edizione |
[2. ed] |
|
|
|
|
|
Descrizione fisica |
|
|
|
|
|
|
Altri autori (Persone) |
|
|
|
|
|
|
Soggetti |
|
20-XX - Group theory and generalizations [MSC 2020] |
20H15 - Other geometric groups, including crystallographic groups [MSC 2020] |
51F15 - Reflection groups, reflection geometries [MSC 2020] |
20F05 - Generators, relations, and presentations of groups [MSC 2020] |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
| |