Low-Power Computer Vision: Status, Challenges, Opportunities

TL;DR

This paper reviews the current state and challenges of low-power computer vision, highlighting recent solutions from the IEEE LPIRC and proposing future research directions for energy-efficient visual systems.

Contribution

It provides a comprehensive summary of low-power computer vision solutions from LPIRC 2018 and suggests new research opportunities in energy-efficient vision systems.

Findings

Summarizes 2018 LPIRC winners' solutions.

Identifies key challenges in low-power vision.

Proposes future research directions.

Abstract

Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots). These systems rely on batteries and energy efficiency is critical. This article serves two main purposes: (1) Examine the state-of-the-art for low-power solutions to detect objects in images. Since 2015, the IEEE Annual International Low-Power Image Recognition Challenge (LPIRC) has been held to identify the most energy-efficient computer vision solutions. This article summarizes 2018 winners' solutions. (2) Suggest directions for research as well as opportunities for low-power computer vision.