부산대학교 도서관

The Internet of Things (IoT) is rapidly enabling applications in many different fields by embedding itself into the physical world. Many potential IoT devices require some level of machine learning or cognitive capability to be truly effective, but the high computational complexity of cognitive algorithms makes them unsuitable for low-power IoT processors. To understand the design challenges of cognitive IoT devices, the authors study a cognitive drone application in a design space called Flying IoT. They find that the computing capability provided by typical drone processors falls far short of satisfying the real-time performance requirements of their application. To improve their processor’s performance on the edge while maintaining its low power consumption and small form factor, the authors propose a sensor-cloud architecture in which data collection is done at the edge and data processing is offloaded to the cloud. Their sensor-cloud architecture can process complex convolution neural network models nearly in real time with software optimizations such as downsampling and compression, while consuming less power than state-of-the art embedded processors such as the Jetson TX1.