  Embodied AI robots have the potential to fundamentally improve the way human
beings live and manufacture. Continued progress in the burgeoning field of
using large language models to control robots depends critically on an
efficient computing substrate. In particular, today's computing systems for
embodied AI robots are designed purely based on the interest of algorithm
developers, where robot actions are divided into a discrete frame-basis. Such
an execution pipeline creates high latency and energy consumption. This paper
proposes Corki, an algorithm-architecture co-design framework for real-time
embodied AI robot control. Our idea is to decouple LLM inference, robotic
control and data communication in the embodied AI robots compute pipeline.
Instead of predicting action for one single frame, Corki predicts the
trajectory for the near future to reduce the frequency of LLM inference. The
algorithm is coupled with a hardware that accelerates transforming trajectory
into actual torque signals used to control robots and an execution pipeline
that parallels data communication with computation. Corki largely reduces LLM
inference frequency by up to 8.0x, resulting in up to 3.6x speed up. The
success rate improvement can be up to 17.3%. Code is provided for
re-implementation. https://github.com/hyy0613/Corki
