  Edge computing offers the distinct advantage of harnessing compute
capabilities on resources located at the edge of the network to run workloads
of relatively weak user devices. This is achieved by offloading computationally
intensive workloads, such as deep learning from user devices to the edge. Using
the edge reduces the overall communication latency of applications as workloads
can be processed closer to where data is generated on user devices rather than
sending them to geographically distant clouds. Specialised hardware
accelerators, such as Graphics Processing Units (GPUs) available in the
cloud-edge network can enhance the performance of computationally intensive
workloads that are offloaded from devices on to the edge. The underlying
approach required to facilitate this is virtualization of GPUs. This paper
therefore sets out to investigate the potential of GPU accelerator
virtualization to improve the performance of deep learning workloads in a
cloud-edge environment. The AVEC accelerator virtualization framework is
proposed that incurs minimum overheads and requires no source-code modification
of the workload. AVEC intercepts local calls to a GPU on a device and forwards
them to an edge resource seamlessly. The feasibility of AVEC is demonstrated on
a real-world application, namely OpenPose using the Caffe deep learning
library. It is observed that on a lab-based experimental test-bed AVEC delivers
up to 7.48x speedup despite communication overheads incurred due to data
transfers.
