AI 中文总结
针对嵌入式视觉线段检测的资源受限问题，提出基于步长算法的低延迟ASIC架构，经45nm工艺验证，功耗与帧率表现优于同类Line Hough Transform方案，适配边缘计算实时需求。
AI 中文摘要
线段检测是嵌入式视觉应用（如自主导航、视觉SLAM和工业检测）中的关键预处理步骤。深度学习方法虽能实现高精度，但需要大量资源，限制了其在资源受限平台上的部署；经典算法效率较高，但存在依赖内容的延迟问题。本文提出一种用于实时线段检测的低延迟ASIC架构，该设计基于步长算法，包含五项ASIC专属特性：带数据复用的基于寄存器的行缓冲、无乘法器的基于MCM的滤波、8类角度量化、用于单周期匹配的类CAM关联存储器，以及优化的重复去除机制。该架构完全流水线化，每时钟周期处理一个像素，延迟确定。采用45nm CMOS工艺综合后，该设计在VGA分辨率下达到325 FPS，全高清分辨率下达到48 FPS，功耗为25.54 mW，面积为0.412 mm²；在125 MHz下，VGA分辨率吞吐量提升至406 FPS，功耗为31.48 mW。与基于Line Hough Transform的90nm ASIC实现相比，所提设计功耗降低49%，帧率提升超1.6倍，非常适合需要实时性能、低功耗和最小面积的边缘计算应用。
英文摘要
Line segment detection is a critical preprocessing step in embedded vision applications such as autonomous navigation, visual SLAM, and industrial inspection. Deep learning methods achieve high accuracy but require substantial resources, limiting their deployment on resource-constrained platforms. Classical algorithms are efficient but exhibit content-dependent latency. This paper presents a low-latency ASIC architecture for real-time line segment detection. The proposed design is based on the step-length algorithm and incorporates five ASIC-specific features: register-based line buffering with data reuse, multiplierless MCM-based filtering, 8-class angle quantization, a CAM-like associative memory for single-cycle matching, and an optimized duplicate removal mechanism. The architecture is fully pipelined and processes one pixel per clock cycle with deterministic latency. Synthesized in a 45nm CMOS process, the design achieves 325 FPS at VGA resolution and 48 FPS at Full HD, with 25.54 mW power consumption and 0.412 mm\textsuperscript{2} area. At 125 MHz, the throughput increases to 406 FPS at VGA resolution with 31.48 mW power consumption. Compared with a 90nm ASIC implementation based on the Line Hough Transform, the proposed design reduces power consumption by 49\% and delivers over 1.6 times higher frame rate. The architecture is well suited for edge-computing applications requiring real-time performance, low power, and minimal area.
Comments8 pages, 6 figs
