  In the "Beyond Moore's Law" era, with increasing edge intelligence,
domain-specific computing embracing unconventional approaches will become
increasingly prevalent. At the same time, adopting a variety of
nanotechnologies will offer benefits in energy cost, computational speed,
reduced footprint, cyber resilience, and processing power. The time is ripe for
a roadmap for unconventional computing with nanotechnologies to guide future
research, and this collection aims to fill that need. The authors provide a
comprehensive roadmap for neuromorphic computing using electron spins,
memristive devices, two-dimensional nanomaterials, nanomagnets, and various
dynamical systems. They also address other paradigms such as Ising machines,
Bayesian inference engines, probabilistic computing with p-bits, processing in
memory, quantum memories and algorithms, computing with skyrmions and spin
waves, and brain-inspired computing for incremental learning and
problem-solving in severely resource-constrained environments. These approaches
have advantages over traditional Boolean computing based on von Neumann
architecture. As the computational requirements for artificial intelligence
grow 50 times faster than Moore's Law for electronics, more unconventional
approaches to computing and signal processing will appear on the horizon, and
this roadmap will help identify future needs and challenges. In a very fertile
field, experts in the field aim to present some of the dominant and most
promising technologies for unconventional computing that will be around for
some time to come. Within a holistic approach, the goal is to provide pathways
for solidifying the field and guiding future impactful discoveries.
