Thermal management in neuromorphic materials, devices, and networks*

 

Computing technology has experienced unprecedented growth in recent years, often referred to as an "artificial intelligence revolution." The human brain inspires the fundamental approach for an energy-efficient new computing paradigm: using neural networks to classify large amounts of data into sorting categories. ^[1,2] Although the implementation of machine learning schemes significantly improves the  computing machine in multiple applications, these approaches are still inefficient in energy consumption, and thermal management. This motivates the search for new hardware-based systems that imitate the brain. ^[1,2,3] In this talk, we describe the current state of thermal management of neuromorphic systems; the role of the brain criticality and resistive switching-based neuromorphic devices ^{[4, 5]}; the energy and electrical considerations for spiking-based computation and their relation with fundamental features of the brain's thermo regulation ^[6]; graph neural networks models in neuromorphic computing ^[7]; and thermoelectric control of materials and neuromorphic devices. ^[3]