Performance Analysis of Image Classification between Edge and Cloud Computing

Image classification has become a major application in the AI era for connecting the physical and digital worlds. However, it requires intensive graphic processing power. IoT and Edge Computing have become popular approaches for distributing and offloading the workload from the cloud to the edge. Many edge devices are powered by an energy-efficient processor that can't execute intensive workloads, but some may be able to. In this paper, we studied the overview of image classification implementation on edge and cloud computing and analyzed the performance to reveal the opportunity to implement a proper system architecture. The edge and cloud computing environments studied in this paper are a smartphone and a cloud GPU instance with sample applications to simulate real-world scenarios. The performance is based on the datasets and the processing environment comprising three factors: ML runtime, hardware, and network. Resulting in six factors: inference time, end-to-end execution time (including network delays), accuracy, confidence score, resource usage, and data transfer.