General-purpose Dataflow Model with Neuromorphic Primitives
This addresses the gap between program versatility and hardware efficiency in neuromorphic computing, offering a domain-specific solution for control flow programs.
The paper tackles the problem of deploying general-purpose programs on neuromorphic hardware by introducing a neuromorphic dataflow model with 'when' and 'where' primitives, which provides a compact and compatible program representation for control logic, enabling improved programmability and performance.
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.