  Spiking Neural Networks (SNNs) are extensively utilized in brain-inspired
computing and neuroscience research. To enhance the speed and energy efficiency
of SNNs, several many-core accelerators have been developed. However,
maintaining the accuracy of SNNs often necessitates frequent explicit
synchronization among all cores, which presents a challenge to overall
efficiency. In this paper, we propose an asynchronous architecture for Spiking
Neural Networks (SNNs) that eliminates the need for inter-core synchronization,
thus enhancing speed and energy efficiency. This approach leverages the
pre-determined dependencies of neuromorphic cores established during
compilation. Each core is equipped with a scheduler that monitors the status of
its dependencies, allowing it to safely advance to the next timestep without
waiting for other cores. This eliminates the necessity for global
synchronization and minimizes core waiting time despite inherent workload
imbalances. Comprehensive evaluations using five different SNN workloads show
that our architecture achieves a 1.86x speedup and a 1.55x increase in energy
efficiency compared to state-of-the-art synchronization architectures.
