HALOC-AxA：面向图像处理应用的面积/能量高效近似加法器
HALOC-AxA: An Area/-Energy-Efficient Approximate Adder for Image Processing Application
AI总结：
本文提出一种新型近似加法器HALOC-AxA，在面积和能量效率上优于现有设计，同时保持或提升精度，并成功应用于图像处理任务以重建高质量图像。
摘要：
近似加法器的设计已被广泛研究，以推进面向计算密集型多媒体应用（如图像、音频或视频处理）的节能硬件。文献中存在多种静态和动态近似加法器，每种都致力于平衡高性能、计算精度和能量效率之间的冲突需求。这项工作引入了一种新颖的近似加法器，它比现有加法器更节能、更节省面积，同时实现了改进或相当的精度，如仿真结果所示。所提出的加法器能够数字重建高质量图像的能力，进一步通过将该设计部署于图像处理任务中得到证明。
英文摘要：
The design of approximate adders has been widely researched to advance energy-efficient hardware for computation-intensive multimedia applications, such as image, audio, or video processing. The design of approximate adders has been widely researched to advance energy-efficient hardware for computation intensive multimedia applications, such as image/audio/video processing. Several static and dynamic approximate adders exist in the literature, each of which endeavors to balance the conflicting demands of high performance, computational accuracy, and energy efficiency. This work introduces a novel approximate adder that is more energy- and area-efficient than existing adders, while achieving improved or comparable accuracy, as demonstrated by simulation results. The proposed adder's ability to digitally reconstruct high quality images is further demonstrated by the deployment of the design for an image processing task.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
