A Tightly Coupled AI-ISP Vision Processor

Hao Zhang, Sicheng Li, Yupeng Gui, Zhiyong Li, Shusong Xu, Yanheng Lu, Dimin Niu, Hongzhong Zheng, Yen-Kuang Chen, Yuan Xie, Yibo Fan

Abstract: To achieve high-quality and high-resolution image processing, this work presents a novel vision processor that facilitates deep learning-enhanced image processing pipelines. At the system level, by identifying that a divide-and-conquer approach is essential to synergize both classical image processing and image enhancement networks, we develop a tightly coupled system with strip-tile conversion dataflow to enable fine-grained low-latency data interactions between image signal processors (ISPs) and the deep learning accelerator (DLA). At the architecture level, we design a comprehensive set of 21 efficient image processing modules to construct classical ISP pipelines, a tile-based strip layer fusion DLA specifically optimized for networks, and a programmable pixel pool that seamlessly supports the data access patterns of the ISP and the DLA. At the software and hardware co-design level, we propose a comprehensive optimization framework to address the implementation overhead of networks while maintaining the image quality. Finally, evaluations of the AI-ISP vision processor demonstrate 53.95% external memory access reduction and 35.51% latency reduction, delivering superior image quality with minimal on-chip memory overhead. A throughput of up to 168.5 frames per second facilitates efficient processing of ultra-high definition (UHD) resolution images.