{"product_id":"hardware-aware-probabilistic-machine-learning-models-learning-inference-and-use-cases-9783030740443","title":"Hardware-Aware Probabilistic Machine Learning Models: Learning, Inference and Use Cases","description":"\u003cp\u003e • Author(s): Laura Isabel Galindez Olascoaga\u003cbr\u003e • Publisher: Springer\u003cbr\u003e • Publisher Imprint: Springer\u003cbr\u003e • BISAC: Electronics - Circuits - General\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFrom the Back Cover\u003c\/b\u003e\u003cbr\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\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 \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.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eIntroduces a new, systematic approach for the realization of hardware-awareness with probabilistic models;\u003c\/li\u003e\n\u003cli\u003eEnables readers to accommodate various systems and applications, as demonstrated with multiple use cases targeting distinct types of devices;\u003c\/li\u003e\n\u003cli\u003eDescribes 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;\u003c\/li\u003e\n\u003cli\u003eRepresents one of the first efforts systematically to bring probabilistic inference to the world of edge computing, by means of novel algorithmic insights and strategies.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cp\u003e\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Paperback","offer_id":45279367987351,"sku":"9783030740443","price":4407.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9783030740443.webp?v=1769293388","url":"https:\/\/atlanticbooks.com\/products\/hardware-aware-probabilistic-machine-learning-models-learning-inference-and-use-cases-9783030740443","provider":"Atlantic Books","version":"1.0","type":"link"}