Latency and Throughput Characterization of Convolutional Neural Networks for Mobile Computer Vision

TL;DR

This paper characterizes the performance of CNNs for mobile vision, analyzing latency and throughput trade-offs across various hardware and software configurations using real workloads.

Contribution

It provides a comprehensive performance analysis of CNN models on multiple hardware platforms and frameworks, highlighting complex latency-throughput behaviors.

Findings

Significant latency-throughput trade-offs exist.

Performance varies greatly with hardware and software choices.

Multiple factors influence CNN performance on mobile and server platforms.

Abstract

We study performance characteristics of convolutional neural networks (CNN) for mobile computer vision systems. CNNs have proven to be a powerful and efficient approach to implement such systems. However, the system performance depends largely on the utilization of hardware accelerators, which are able to speed up the execution of the underlying mathematical operations tremendously through massive parallelism. Our contribution is performance characterization of multiple CNN-based models for object recognition and detection with several different hardware platforms and software frameworks, using both local (on-device) and remote (network-side server) computation. The measurements are conducted using real workloads and real processing platforms. On the platform side, we concentrate especially on TensorFlow and TensorRT. Our measurements include embedded processors found on mobile devices and high-performance processors that can be used on the network side of mobile systems. We show that there exists significant latency--throughput trade-offs but the behavior is very complex. We demonstrate and discuss several factors that affect the performance and yield this complex behavior.