Fast AdaBoost-Based Face Detection System on a Dynamically Coarse Grain Reconfigurable Architecture

Abstract

An AdaBoost-based face detection system is proposed, on a Coarse Grain Reconfigurable Architecture (CGRA) named “REMUS-II”. Our work is quite distinguished from previous ones in three aspects. First, a new hardware-software partition method is proposed and the whole face detection system is divided into several parallel tasks implemented on two Reconfigurable Processing Units (RPU) and one micro Processors Unit (µPU) according to their relationships. These tasks communicate with each other by a mailbox mechanism. Second, a strong classifier is treated as a smallest phase of the detection system, and every phase needs to be executed by these tasks in order. A phase of Haar classifier is dynamically mapped onto a Reconfigurable Cell Array (RCA) only when needed, and it's quite different from traditional Field Programmable Gate Array (FPGA) methods in which all the classifiers are fabricated statically. Third, optimized data and configuration word pre-fetch mechanisms are employed to improve the whole system performance. Implementation results show that our approach under 200MHz clock rate can process up-to 17 frames per second on VGA size images, and the detection rate is over 95%. Our system consumes 194mW, and the die size of fabricated chip is 23mm2 using TSMC 65nm standard cell based technology. To the best of our knowledge, this work is the first implementation of the cascade Haar classifier algorithm on a dynamically CGRA platform presented in the literature.