Unconventional computing moves away from the classical Turing/von Neumann/gate logic paradigms for designing, building, programming, and applying computational devices.  This includes non-symbolic computing, in memory computing, non-boolean logics, computing exploiting physical properties including quantum computing, brain-inspired computing, computing in unconventional substrates, and more.

Theory, models, and simulations

Theory of unconventional computation, physics of computation, chaos and stochastic computing complexity.

Physical models, analog (real valued) computing, in materio computing, dynamical systems computing, cellular automata, spin ice models, nature-inspired algorithms including neuromorphic systems and reservoir computers, quantum computing.

Substrate and device simulations: atomistic, phenomenological, learned.

Hardware devices and implementations

nanoelectronics (SET, SAT, nanoparticles, atomic switch networks, dopant networks, memristors), spintronics (impurities in semiconductors, MTJ, STT, skyrmions, artificial spin-ice), photonics, neuroelectronics, quantum reservoir computing, mechanical systems, chemical and bio-chemical systems, biological systems.

Algorithms and applications

Use: Evaluating computational capacity, programming hardware devices.

Problem domains: Complex systems time series prediction, edge computing and filtering, transduction.

Specific applications: smart sensors, health-care, human-robot interaction, music.