Neuromorphic Computing: From Quantum Materials to Emergent Connectivity
Over the last decades, as the slowdown of Moore's Law becomes increasingly imminent, various solutions beyond the Turing/von-Neumann computation paradigms have emerged. One of them takes inspiration from the brain—neuromorphic computing—which aims to mimic the collective behavior of biological neurons, synapses, axons, dendrites, etc. The field has grown into a proven, feasible approach but still has remaining challenges such as achieving the energy efficiency of the brain. The key ingredients to develop an energy-efficient neuromorphic computer include new material and functional platforms (where quantum materials have recently become prominent candidates), devices and connectivity types, systems architectures, and mathematical algorithms. It is a timely moment to collect the community's thoughts on the state-of-the-art of neuromorphic computing and discuss the key advances and difficult challenges to address.
Ivan Schuller, Alex Frano, Jian Shen, Axel Hoffmann, Robert Dynes, Abu Sebastian, Catherine Schuman, Anirban Bandyopadhyay, and Beatriz Noheda