ASNA-Flow: An Efficient Asynchronous Neuromorphic Accelerator for Real-Time Event-Based Optical Flow

Optical flow estimation constitutes a fundamental computational challenge in computer vision, with critical applications object trajectory prediction, depth reconstruction, and autonomous navigation systems. The emergence of neuromorphic vision systems, integrating event-driven cameras with spiking neural networks (SNNs), has recently gained attention as a promising paradigm for edge deployment of optical flow estimation due to their advantages in ultralow power and resource efficiency. However, current neuromorphic computing platforms lack specialized architectures optimized for this problem domain. Existing implementations either prioritize configurable architectures at the expense of energy efficiency or employ intricate hardware control mechanisms to manage the asynchronous and sparse computing patterns inherent in SNNs. To address these limitations, we present ASNA-Flow, an event-driven asynchronous neuromorphic accelerator featuring a pioneering algorithm–hardware co-design framework specifically tailored for event-based optical flow estimation. Our methodology encompasses three key innovations: 1) a hardware-aware algorithm optimization that maintains computational fidelity while enhancing implementation efficiency; 2) systematic data pattern analysis to inform architectural decisions; and 3) novel exploitation of optical flow’s spatial locality characteristics to enable efficient sparse computing. Implemented in TSMC 28-nm CMOS technology, ASNA-Flow achieves real-time performance of 104 frames per second (FPS) with ultralow power consumption of 7.9 mW, demonstrating superior energy efficiency of 0.3 pJ per synaptic operation (SOP). This work establishes the first dedicated neuromorphic computing solution that simultaneously addresses the temporal sparsity, event-driven processing, and energy constraints inherent in optical flow estimation tasks.