{"product_id":"9783030740412","title":"Hardware-Aware Probabilistic Machine Learning Models: Learning, Inference and Use Cases","description":"\u003ch1\u003eHardware-Aware Probabilistic Machine Learning Models: Learning, Inference and Use Cases\u003c\/h1\u003e \u003ch2\u003eGalindez Olascoaga, Laura Isabel; Meert, Wannes; Verhelst, Marian\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis 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. \u003c\/p\u003e\n\n\u003cp\u003eThe 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.\u003c\/p\u003e\n\n\u003cp\u003eThe 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.\u003cb\u003e\u003ci\u003e \u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2021-05-20\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9783030740412\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-3-030-74042-9\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 163\u003c\/p\u003e ","brand":"Springer International Publishing","offers":[{"title":"Default Title","offer_id":47531300356236,"sku":"9783030740412","price":80.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9783030740412.jpg?v=1776051435","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9783030740412","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}