Reconfigurable neuromorphic components and algorithms for next-generation artificial intelligence
Digital transistor-based general-purpose hardware (e.g., central processing units) is the dominant solution to support both traditional computing (logic, arithmetic, etc.) as well as modern artificial intelligence. State-of-the-art research has shown feasibility of post-digital physics-based neuromorphic hardware, which is hypothesized to support artificial intelligence algorithms with orders-of-magnitude improved time/energy efficiencies. But such research has not been widely deployed mainly because of such novel hardware’s extreme application-specificity, and the dominance of low-cost general-purpose (but inefficient) digital hardware. To make use of the novel algorithms and the superlative performance of physics-based hardware, we need to identify scientific principles that can enable generality in physics-based hardware. This work resulted in two important broad outcomes – first, we demonstrate fully reconfigurable neuromorphic components, and second, we demonstrate a viable artificial intelligence learning algorithm that can exploit the functioning of neuromorphic hardware. We demonstrate up to five orders of magnitude improvement in energy efficiency compared to the best general-purpose digital hardware.