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Neuromorphic Computation Using Quantum-Dot Cellular Automata

Quantum-dot cellular automata (QCA) is a paradigm for low-power, general-purpose, classical computing beyond the transistor era. In classical QCA, the elementary device is a cell, a system of quantum dots with a few mobile charges occupying some dots. Device switching is achieved by quantum mechanical tunneling between dots, and cells are interconnected locally via the electrostatic field. Logic is constructed by laying out arrays of QCA cells on a two-dimensional substrate. Several different implementations of QCA exist. Neuromorphic computing is computing which mimics aspects of how our brains compute. This includes parallel processing using highly interconnected primitives which combine local processing and memory. Viable neuron-like devices suitable for neuromorphic computation require a weighted signal fan-in, a way to aggregate signals, and a spike (pulse) output mechanism. The inputs to a neuron can be "excitatory" or "inhibitory" which refers to their ability to encourage or discourage a neuron to fire. We briefly review the concept of QCA and discuss how QCA cells satisfy these requirements. Viable implementations for QCA-based neuromorphism, and challenges that exist for implementing neuromorphic devices in QCA also will be discussed.