Abstract: Neuromorphic processors show extremely high energy efficiency advantages over traditional deep learning processors. The network-on-chip with high scalability, high throughput, and high versatility features is generally adopted as the on-chip communication and connection implementation of neuromorphic processors. In order to solve the problems of making the synchronous network-on-chip that adopts the global clock tree to achieve timing closure, matching link delay in the asynchronous network-on-chip, and lacking electronic design automation tools in implementation and verification of asynchronous network-on-chip, we propose a low-power asynchronous network-on-chip architecture, NosralC, to build a global-asynchronous-local-synchronous multi-core neuromorphic processor. NosralC is implemented with asynchronous links and synchronous routers. The small amount of asynchronous design makes NosralC similar to the synchronous design and friendly to implementation and validation of asynchronous design using existing electronic design automation tools. Experiments show that compared with a synchronous counterpart baseline with the same function, NosralC achieves 37.5%−38.9% reduction in power consumption, 5.5%−8.0% reduction in average latency, and 36.9%−47.6% improvement in energy efficiency in executing the FSDD, DVS128 Gesture, NTI-DIGITS, and NMNIST neuromorphic application datasets while increasing less than 6% additional resource overhead and a small amount of performance overhead (0.8%−2.4% throughput decrease). NosralC is verified on the field programmable gate array (FPGA) platform and its implementability is proved.