Share
Hardware-Aware Probabilistic Machine Learning Models: Learning, Inference and Use Cases (in English)
Marian Verhelst
(Author)
·
Laura Isabel Galindez Olascoaga
(Author)
·
Wannes Meert
(Author)
·
Springer
· Paperback
Hardware-Aware Probabilistic Machine Learning Models: Learning, Inference and Use Cases (in English) - Galindez Olascoaga, Laura Isabel ; Meert, Wannes ; Verhelst, Marian
$ 61.57
$ 64.99
You save: $ 3.42
Choose the list to add your product or create one New List
✓ Product added successfully to the Wishlist.
Go to My WishlistsIt will be shipped from our warehouse between
Monday, July 08 and
Tuesday, July 09.
You will receive it anywhere in United States between 1 and 3 business days after shipment.
Synopsis "Hardware-Aware Probabilistic Machine Learning Models: Learning, Inference and Use Cases (in English)"
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.
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
All books in our catalog are Original.
The book is written in English.
The binding of this edition is Paperback.
✓ Producto agregado correctamente al carro, Ir a Pagar.
