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This book explores the intersection between analog signals, neuromorphic systems, and computing processors. Here, we find a class of computing devices able to solve biological or deep learning tasks by setting up an analog model of the system of interest. While graphics processing units played a pivotal role in enabling the recent deep learning breakthrough, the advancement of the AI era is posing a pressing demand for novel computing, communications, and information processing capabilities. This shifting landscape has sparked a renaissance in analog processors. Far from trying to replace digital CPUs, this specialized hardware is designed to complement them - pushing the boundaries of computing by (I) enabling new computing paradigms or (II) achieving levels of performance that are otherwise unattainable.

Divided into two parts, the book discusses what analog computing is and why it has the potential to be the key enabling technology for the next generation of AI hardware. The first part discusses the fundamental concepts necessary to understand analog neuromorphic processors, from an introduction to modern AI and its models, to the discussion of its shortcomings and ending with an in-depth discussion on analog signals. The second part presents electronic and photonic analog architectures encompassing memristor arrays, spiking neurons, and reservoir computing. This book aims to introduce AI and analog devices at various levels of analysis to anyone interested in the field, from science enthusiasts to graduate students and professionals. Its structured and pedagogical approach also makes it a prime candidate for textbook adoption in suitable university-level courses.