  There is a growing demand for low-power, autonomously learning artificial
intelligence (AI) systems that can be applied at the edge and rapidly adapt to
the specific situation at deployment site. However, current AI models struggle
in such scenarios, often requiring extensive fine-tuning, computational
resources, and data. In contrast, humans can effortlessly adjust to new tasks
by transferring knowledge from related ones. The concept of learning-to-learn
(L2L) mimics this process and enables AI models to rapidly adapt with only
little computational effort and data. In-memory computing neuromorphic hardware
(NMHW) is inspired by the brain's operating principles and mimics its physical
co-location of memory and compute. In this work, we pair L2L with in-memory
computing NMHW based on phase-change memory devices to build efficient AI
models that can rapidly adapt to new tasks. We demonstrate the versatility of
our approach in two scenarios: a convolutional neural network performing image
classification and a biologically-inspired spiking neural network generating
motor commands for a real robotic arm. Both models rapidly learn with few
parameter updates. Deployed on the NMHW, they perform on-par with their
software equivalents. Moreover, meta-training of these models can be performed
in software with high-precision, alleviating the need for accurate hardware
models.
