Roadmap for Unconventional Computing with Nanotechnology

Abstract

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.

arXiv comment80 pages accepted in Nano Futures

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Cited by in corpus (17)

• Real-Time Decoding for Fault-Tolerant Quantum Computing: Progress, Challenges and Outlook
• Integrated probabilistic computer using voltage-controlled magnetic tunnel junctions as its entropy source
• Wavenumber-dependent magnetic losses in YIG-GGG heterostructures at millikelvin temperatures
• Elimination of substrate-induced FMR linewidth broadening in the epitaxial system YIG-GGG by microstructuring
• Engineering-Oriented Design of Drift-Resilient MTJ Random Number Generator via Hybrid Control Strategies
• Integration of Cobalt Ferromagnetic Control Gates for Electrical and Magnetic Manipulation of Semiconductor Quantum Dots
• Mutual synchronization in spin torque and spin Hall nano-oscillators
• Edge-of-chaos enhanced quantum-inspired algorithm for combinatorial optimization
• Creating currents of electric bubbles
• Tunable Random Telegraph Noise in Stable Perpendicular Magnetic Tunnel Junctions for Unconventional Computing
• Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines
• Ground-State Probabilistic Logic with the Simplest Binary Energy Landscape for Probabilistic Computing
• Prolegomena To Any Future Device Physics
• Polaritonic Machine Learning for Graph-based Data Analysis
• Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems
• A Toffoli Gadget for Magnetic Tunnel Junctions Boltzmann Machines
• Beyond conventional skyrmions in synthetic antiferromagnets