Musical Chair: Efficient Real-Time Recognition Using Collaborative IoT Devices
Ramyad Hadidi, Jiashen Cao, Matthew Woodward, Michael S. Ryoo, Hyesoon, Kim

TL;DR
Musical Chair is a distributed system that leverages collaborative IoT devices to perform real-time recognition tasks efficiently without relying on cloud computing, adapting dynamically to device availability.
Contribution
It introduces a novel approach for distributed deep neural network processing across resource-constrained IoT devices, enabling efficient real-time recognition.
Findings
Achieves twice the performance of a high-end embedded platform.
Maintains similar energy consumption to low-power devices.
Adapts dynamically to IoT network changes.
Abstract
The prevalence of Internet of things (IoT) devices and abundance of sensor data has created an increase in real-time data processing such as recognition of speech, image, and video. While currently such processes are offloaded to the computationally powerful cloud system, a localized and distributed approach is desirable because (i) it preserves the privacy of users and (ii) it omits the dependency on cloud services. However, IoT networks are usually composed of resource-constrained devices, and a single device is not powerful enough to process real-time data. To overcome this challenge, we examine data and model parallelism for such devices in the context of deep neural networks. We propose Musical Chair to enable efficient, localized, and dynamic real-time recognition by harvesting the aggregated computational power from the resource-constrained devices in the same IoT network as…
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Taxonomy
TopicsHuman Pose and Action Recognition · Video Surveillance and Tracking Methods · Advanced Neural Network Applications
