  In this paper we present an algorithm-hardware codesign for camera-based
autonomous flight in small drones. We show that the large write-latency and
write-energy for nonvolatile memory (NVM) based embedded systems makes them
unsuitable for real-time reinforcement learning (RL). We address this by
performing transfer learning (TL) on metaenvironments and RL on the last few
layers of a deep convolutional network. While the NVM stores the meta-model
from TL, an on-die SRAM stores the weights of the last few layers. Thus all the
real-time updates via RL are carried out on the SRAM arrays. This provides us
with a practical platform with comparable performance as end-to-end RL and
83.4% lower energy per image frame
