Outlook for Full Hardware Implementation of Neuromorphic Architectures Using Quantum Materials*

        Data manipulation in its many forms drives and fuels our civilization. Revolutionary developments in the past decades in hardware (principally CMOS technology) and software (such as machine-learning and artificial intelligence), has fueled the ever-increasing capabilities of modern computational machines. It is however agreed that these enhanced computational capabilities will soon slow down considerably, due to a variety of limitation, principally because of the large energy consumption expected in existing CMOS based hardware. On the other hand, nature has evolved a energy efficient computational machine (the “brain”) which has substantial advantages over conventional silicon-based computers.

        I will report the effort of a large number of investigators collaborating to design and investigate the use of quantum materials to develop energy efficient neuromorphic architecture. In particular, I will describe a novel class of “thermal neuristor” based on spiking oscillators which function and communicate through thermal processes. These neuristors exhibit a wide range of electrical behavior that closely resembles that of biological neurons including: all-or-nothing law, type-II neuronal rate coding, spike-in and DC out, spike-in and spike-out, and stochastic leaky integrate-and-firing. Remarkably, inhibitory capabilities are achieved using just a single oxide device, and the transmission of cascaded information occurs solely through thermal interactions without any intricate circuits. This research provides some of the groundwork for scalable, energy-efficient, thermal neural networks, advancing the field of brain-inspired computing.