Accelerating gesture recognition algorithm using coarse grained reconfigurable architectures

The gesture recognition algorithms have been widely used to realize the human-computer interaction in multimedia applications, but still have limitation of integration due to the computation overhead. For this reason, this paper proposes an acceleration method for the hand gesture recognition algorithm using CGRA. The proposed algorithm enables software pipelining and vectorization for the SRP architecture, which exploits both the instruction level parallelism and data level parallelism. Therefore, the proposed optimization method improves the utilization of the hardware resources in SRP and effectively accelerates the performance of the gesture recognition algorithm, achieving maximum speedup of 10.97.