Reaching for the Sky: Maximizing Deep Learning Inference Throughput on Edge Devices with AI Multi-Tenancy

Abstract

The wide adoption of smart devices and Internet-of-Things (IoT) sensors has led to massive growth in data generation at the edge of the Internet over the past decade. Intelligent real-time analysis of such a high volume of data, particularly leveraging highly accurate deep learning (DL) models, often requires the data to be processed as close to the data sources (or at the edge of the Internet) to minimize the network and processing latency. The advent of specialized, low-cost, and power-efficient edge devices has greatly facilitated DL inference tasks at the edge. However, limited research has been done to improve the inference throughput (e.g., number of inferences per second) by exploiting various system techniques. This study investigates system techniques, such as batched inferencing, AI multi-tenancy, and cluster of AI accelerators, which can significantly enhance the overall inference throughput on edge devices with DL models for image classification tasks. In particular, AI multi-tenancy enables collective utilization of edge devices’ system resources (CPU, GPU) and AI accelerators (e.g., Edge Tensor Processing Units; EdgeTPUs). The evaluation results show that batched inferencing results in more than 2.4× throughput improvement on devices equipped with high-performance GPUs like Jetson Xavier NX. Moreover, with multi-tenancy approaches, e.g., concurrent model executions (CME) and dynamic model placements (DMP), the DL inference throughput on edge devices (with GPUs) and EdgeTPU can be further improved by up to 3× and 10×, respectively. Furthermore, we present a detailed analysis of hardware and software factors that change the DL inference throughput on edge devices and EdgeTPUs, thereby shedding light on areas that could be further improved to achieve high-performance DL inference at the edge.