人脸检测系统应用在嵌入式环境中需满足多种约束，高计算密集性、控制密集性是实时实现困难的主要原因. 文中提出一种基于名为“REMUS-II”的粗粒度动态可重构架构的人脸检测系统，把层叠型AdaBoost检测算法划分成多个非连续子任务，通过邮箱通信调度、配置流和数据流优化方法来提高指令级并行度和任务级并行度.实验结果表明，检测分辨率为640£480 的图片可获得17 帧/s 的平均检测速度，正面人脸检测率保持在95% 以上.在TSMC 65 nm CMOS工艺、200 MHz工作频率下，REMUS-II 面积约为24 mm2，功率约为194 mW.

Face detection system needs to meet a variety of constraints in embedded environments, but the high computational/control intensive features make the real-time implementation difficult. This paper presents a face detection system based on a dynamical coarse-grained partially reconfigurable platform called “REMUSII”. The cascade AdaBoost-based detection algorithm is divided into several non-consecutive sub-tasks. Mailbox
scheduling, configuration flow and data flow optimization methods improve the instruction-level and task-level parallelism. Experiment results show that this approach with a 200 MHz clock can process about 17 frames per second on 640£480 images. Its detection rate is over 95%. The system consumes about 194 mW, and its area is about 24 mm2 in TSMC’s 65 nm logic process.