Ecrt

Jiang, Zhehao; Ling, Neiwen; Shuai, Xian; Xing, Guoliang

doi:10.1145/3274783.3275199

Cited by 33 publications

(2 citation statements)

References 1 publication

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“…However, the algorithm was not deployed on a real hardware device, and the impact of their frame rate control on energy consumption was only analyzed theoretically via simulations. Zhao et al propose adopting the NVIDIA Jetson TX2 as an edge server for real-time object tracking [42]. In their demo paper, the authors propose partitioning a YOLOv2tiny across a Raspberry Pi 3B+ (which acts as the IoT enddevice) and the Jetson TX2.…”

Section: Object Detection and Tracking At The Edgementioning

confidence: 99%

A Power-aware Vision-based Virtual Sensor for Real-Time Edge Computing

Contoli,

Calisti,

Fabrizio

et al. 2024

Preprint

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Graphics Processing Units and Tensor Processing Units coupled with tiny machine learning models deployed on edge devices are revolutionizing computer vision and real-time tracking systems. However, edge devices often pose constraints regarding computational resources and power consumption. This paper proposes a visual-based virtual sensor paradigm that provides power-aware multi-object tracking at the edge while preserving tracking accuracy and enhancing privacy. The virtual sensor implements a new Dynamic Inference Power Manager (DIPM) based on an adaptive frame rate. We implement and deploy the virtual sensor and the DIPM on the NVIDIA Jetson Nano edge platform to prove the effectiveness and efficiency of our solutions. Extensive experimental results show that the proposed virtual sensor can achieve a 40% reduction in energy consumption, with a marginal decrease of less than 6% in tracking accuracy.

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Section: Object Detection and Tracking At The Edgementioning

confidence: 99%

A Power-aware Vision-based Virtual Sensor for Real-Time Edge Computing

Contoli,

Calisti,

Fabrizio

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…For example, currently deployed level 3 autonomous vehicles [150], [243], [19] primarily rely on vision sensors and GPU computing systems, consuming significant resources in terms of memory and energy, respectively. When integrating these ADAS features into resource-constrained [101], [395], [284], [339] and energy-constrained [361], [378], [178], [386] real-time autonomous systems, several challenges arise.…”

Section: Approximate Techniquesmentioning

confidence: 99%

A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services

Katare

Perino

Nurmi

et al. 2023

IEEE Commun. Surv. Tutorials

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Autonomous driving services depends on active sensing from modules such as camera, LiDAR, radar, and communication units. Traditionally, these modules process the sensed data on high-performance computing units inside the vehicle, which can deploy intelligent algorithms and AI models. The sensors mentioned above can produce large volumes of data, potentially reaching up to 20 Terabytes. This data size is influenced by factors such as the duration of driving, the data rate, and the sensor specifications. Consequently, this substantial amount of data can lead to significant power consumption on the vehicle. Similarly, a substantial amount of data will be exchanged between infrastructure sensors and vehicles for collaborative vehicle applications or fully connected autonomous vehicles. This communication process generates an additional surge of energy consumption. Although the autonomous vehicle domain has seen advancements in sensory technologies, wireless communication, computing and AI/ML algorithms, the challenge still exists in how to apply and integrate these technology innovations to achieve energy efficiency. This survey reviews and compares the connected vehicular applications, vehicular communications, approximation and Edge AI techniques. The focus is on energy efficiency by covering newly proposed approximation and enabling frameworks. To the best of our knowledge, this survey is the first to review the latest approximate Edge AI frameworks and publicly available datasets in energy-efficient autonomous driving. The insights from this survey can benefit the collaborative driving service development on low-power and memory-constrained systems and the energy optimization of autonomous vehicles.

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A power-aware vision-based virtual sensor for real-time edge computing

Contoli,

Calisti,

Fabrizio

et al. 2024

J Real-Time Image Proc

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Graphics processing units and tensor processing units coupled with tiny machine learning models deployed on edge devices are revolutionizing computer vision and real-time tracking systems. However, edge devices pose tight resource and power constraints. This paper proposes a real-time vision-based virtual sensors paradigm to provide power-aware multi-object tracking at the edge while preserving tracking accuracy and enhancing privacy. We thoroughly describe our proposed system architecture, focusing on the Dynamic Inference Power Manager (DIPM). Our proposed DIPM is based on an adaptive frame rate to provide energy savings. We implement and deploy the virtual sensor and the DIPM on the NVIDIA Jetson Nano edge platform to prove the effectiveness and efficiency of the proposed solution. The results of extensive experiments demonstrate that the proposed virtual sensor can achieve a reduction in energy consumption of about 36% in videos with relatively low dynamicity and about 21% in more dynamic video content while simultaneously maintaining tracking accuracy within a range of less than 1.2%.

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Ecrt

Cited by 33 publications

References 1 publication

A Power-aware Vision-based Virtual Sensor for Real-Time Edge Computing

A Power-aware Vision-based Virtual Sensor for Real-Time Edge Computing

A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services

A power-aware vision-based virtual sensor for real-time edge computing

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