Porting SYCL accelerated neural network frameworks to edge devices

Abstract

Portable hardware acceleration has become increasingly necessary with the rise of the popularity of edge computing. Edge computing, referring to the distributed computing paradigm that encourages data to be processed and stored as close to the source of origination as possible, is needed in areas where bandwidth and latency are restricted and network stability, privacy, or security are unreliable or insecure. Examples of such situations are autonomous mobile robotics, such as autonomous tractors, which often have numerous cameras connected to the host, all needing processing in areas where there can be no reliable connection to a cloud-based platform. Additionally, bridge surveying drones, where mapping and path-planning are needed with low latency, can benefit from a lightweight, compact, low-powered device, especially when there are size and energy consumption requirements.