LETI: Latency Estimation Tool and Investigation of Neural Networks inference on Mobile GPU

TL;DR

This paper introduces LETI, a tool for accurate latency estimation of neural network inference on mobile GPUs, addressing the limitations of FLOPs-based proxies and lookup tables, enabling better neural architecture search and performance analysis.

Contribution

The authors develop an open-source latency estimation tool specifically for mobile GPUs, with a data-driven regression model for precise latency prediction tailored to individual devices.

Findings

Latency prediction models achieve good accuracy on target devices.

The approach is validated on NAS-Benchmark 101 and popular neural architectures.

LETI facilitates neural architecture search and large-scale latency evaluation.

Abstract

A lot of deep learning applications are desired to be run on mobile devices. Both accuracy and inference time are meaningful for a lot of them. While the number of FLOPs is usually used as a proxy for neural network latency, it may be not the best choice. In order to obtain a better approximation of latency, research community uses look-up tables of all possible layers for latency calculation for the final prediction of the inference on mobile CPU. It requires only a small number of experiments. Unfortunately, on mobile GPU this method is not applicable in a straight-forward way and shows low precision. In this work, we consider latency approximation on mobile GPU as a data and hardware-specific problem. Our main goal is to construct a convenient latency estimation tool for investigation(LETI) of neural network inference and building robust and accurate latency prediction models for each specific task. To achieve this goal, we build open-source tools which provide a convenient way to conduct massive experiments on different target devices focusing on mobile GPU. After evaluation of the dataset, we learn the regression model on experimental data and use it for future latency prediction and analysis. We experimentally demonstrate the applicability of such an approach on a subset of popular NAS-Benchmark 101 dataset and also evaluate the most popular neural network architectures for two mobile GPUs. As a result, we construct latency prediction model with good precision on the target evaluation subset. We consider LETI as a useful tool for neural architecture search or massive latency evaluation. The project is available at https://github.com/leti-ai