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TinyML Hardware Architectures for Sustainable Smart Cities
ID：70 View protection：Participant Only Updated time：2026-10-04 23:31:53 Views：17 Online

Start Time：2026-10-13 14:30

Duration：15min

Session：[S8] Mixed Track [S8-1] Mixed Track

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Abstract
The emergence of smart cities has resulted in the increasing use of IoT devices for environmental monitoring, intelligent transport, public safety, energy control, and healthcare applications. Nevertheless, the perpetual streaming of sensor data to cloud servers creates high energy costs, latency, communication overhead, and privacy issues. TinyML is a novel paradigm that allows for AI inference in small edge devices that operate with a power budget of only a few milliwatts. In this paper, we present an extensive study of TinyML hardware architectures towards sustainable smart city applications. Our framework combines low-power microcontrollers, edge AI accelerators, efficient communication components, and renewable energy harvesting technologies for enabling on-the-fly inference at the edge of the network. The analysis shows that compared to traditional cloud-based AI systems, the energy consumption and latency can be reduced by up to 70% and 90%, respectively, while reducing network traffic by 80% and increasing the lifetime of sensor nodes by more than 300%.
 
Keywords
TinyML,Smart Cities,Edge AI,Low-Power Computing,Artificial Intelligence,Process Innovation
Speaker
Wai Yie Leong
INTI International University

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Important Dates

  • Conference date

    10-11

    2026

    -

    10-14

    2026

  • 12-30 2025

    Presentation submission deadline

  • 09-28 2026

    Early Bird Registration

  • 10-14 2026

    Draft paper submission deadline

  • 10-15 2026

    Registration deadline

Sponsored By

United Societies of Science

Organized By

Posts and Telecommunications Institute of Technology

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