Profiling Heterogeneous Multi-GPU Systems to Accelerate Cortically Inspired Learning Algorithms
Recent advances in neuroscientific understanding make parallel computing devices modeled after the human neocortex a plausible, attractive, fault-tolerant, and energy efficient possibility. Such attributes have once again sparked an interest in creating learning algorithms that aspire to reverse engineer many of the abilities of the brain. In this paper the authors describe a GPGPU-accelerated extension to an intelligent learning model inspired by the structural and functional properties of the mammalian neocortex. The cortical network, like the brain, exhibits massive amounts of processing parallelism, making today's GPGPUs a highly attractive and readily-available hardware accelerator for such a model.