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Hardware-Aware Probabilistic Machine Learning Models

Name: Hardware-Aware Probabilistic Machine Learning Models
Brand: Springer Nature Switzerland AG
SKU: 9783030740412
Price: 30759 HUF
Availability: InStock

Szerző: Marian Verhelst, Wannes Meert

Nyelv:

Kötés: Kemény kötésű

Kiadó: Springer Nature Switzerland AG

Elérhetőség: Beszállítói készleten

Küldés 10-13 napon belül

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This book proposes probabilistic machine learning models that represent the hardware properties of t...

Információk a könyvről

Szerző

Marian Verhelst, Wannes Meert

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2021

oldal

176

EAN

9783030740412

ISBN

3030740412

Enbook ID

36186665

Kiadó

Springer Nature Switzerland AG

Súly

436

Méretek

160 x 241 x 15

Teljes leírás

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.

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Hardware-Aware Probabilistic Machine Learning Models

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