Neuro-inspired computing with emerging memories: where device physics meets learning algorithms

Modern cognitive computing workloads require computing systems tailored to the applications, where the underlying hardware fabrics should naturally match the characteristics of learning algorithms and compute kernels. With emerging memory technologies (e.g., resistive RAM (RRAM), magnetic RAM (MRAM)), we design neuro-inspired computing systems that exploit technology characteristics such as rich device physics, circuit architecture, and integration capabilities with CMOS and beyond-CMOS technologies. Our methodology is built upon a combination of experimental characterization, cross-stack modeling, and system integration, illustrated by case studies for neural networks and highdimensional (HD) computing. Finally, we discuss the prospects of heterogeneous learning machines that emphasize the integration of compute kernels and learning algorithms, as well as the integration of emerging nanotechnologies.