  Neuromorphic computing exhibits great potential to provide high-performance
benefits in various applications beyond neural networks. However, a
general-purpose program execution model that aligns with the features of
neuromorphic computing is required to bridge the gap between program
versatility and neuromorphic hardware efficiency. The dataflow model offers a
potential solution, but it faces high graph complexity and incompatibility with
neuromorphic hardware when dealing with control flow programs, which decreases
the programmability and performance. Here, we present a dataflow model tailored
for neuromorphic hardware, called neuromorphic dataflow, which provides a
compact, concise, and neuromorphic-compatible program representation for
control logic. The neuromorphic dataflow introduces "when" and "where"
primitives, which restructure the view of control. The neuromorphic dataflow
embeds these primitives in the dataflow schema with the plasticity inherited
from the spiking algorithms. Our method enables the deployment of
general-purpose programs on neuromorphic hardware with both programmability and
plasticity, while fully utilizing the hardware's potential.
