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. ****
