Detailed info

Authors

Francesco Paissan, Massimo Gottardi, Elisabetta Farella

Title

Enabling energy efficient machine learning on a Ultra-Low-Power vision sensor for IoT

Abstract

The Internet of Things (IoT) and smart city paradigm includes ubiquitous technology to extract context information in order to return useful services to users and citizens. An essential role in this scenario is often played by computer vision applications, requiring the acquisition of images from specific devices. The need for high-end cameras often penalizes this process since they are power-hungry and ask for high computational resources to be processed. Thus, the availability of novel low-power vision sensors, implementing advanced features like in-hardware motion detection, is crucial for computer vision in the IoT domain. Unfortunately, to be highly energy-efficient, these sensors might worsen the perception performance (e.g., resolution, frame rate, color). Therefore, domain-specific pipelines are usually delivered in order to exploit the full potential of these cameras. This paper presents the development, analysis, and embedded implementation of a realtime detection, classification and tracking pipeline able to exploit the full potential of background filtering Smart Vision Sensors (SVS). The power consumption obtained for the inference – which requires 8ms – is 7.5 mW.

ISSN

arXiv:2102.01340

Conference

SLOHA 2021 – DATE Friday Workshop on System-level Design Methods for Deep Learning on Heterogeneous Architectures

Date

05/02/2021

Location

Virtual Event

Year of Publication:

2021

10.48550/arXiv.2102.01340