Accelerating CSRN based face recognition on an NVIDIA GPGPU
Tarek M. Taha, Kenneth L. Rice, Ronald H. Miller, Khan M. Iftekharuddin, Michael Hutt, Keith Anderson, Teddy Salan · Infotech@Aerospace 2011 · 2011
Unmanned aerial vehicles (UAVs) are being equipped with high definition cameras to survey a wide range of low-contrast and diverse environments. From data captured by UAVs, image analysts can determine adversarial threats proficiently. However, there is simply too much data and not enough analysts to do this processing efficiently. Enabling computing systems to mimic the processes in the human brain to process sych data would be of significant benefit. CSRNs (cellular simultaneous recurrent networks) are capable of solving several spatial processing tasks that are carried out by human. In particular, they have been shown to be capable of pose invariant face recognition. Given the highly recurrent nature of CSRNs (a property also seen in the human cortex), the computational demands of these algorithms grow with input size. Therefore the acceleration of CSRNs would be highly beneficial. In this paper we examine the acceleration of CSRNs applied to face recognition. We develop optimized implementations of the algorithm on an Intel Xeon 2.67 GHz processor and an NVIDIA Tesla C2050 GPGPU (general purpose graphical processing unit). Our results show that the GPGPU is 22.9 times faster than the CPU implementation.