Comparative Analysis of Edge AI Hardware Platforms for Wearable Assistive Technologies

Author(s)	Devang Wangde, Chaitanya Mule, Dweep Vartak, Vidya Sagvekar
Country	India
Abstract	This review paper presents a comparative analysis of AI hardware platforms for edge devices, focusing on their suitability for wearable assistive technology applications. As edge computing in AI continues to expand, numerous hardware solutions have emerged, each optimized for specific performance metrics such as inference speed, energy consumption, and processing capability. By reviewing six recent studies on edge AI hardware, this paper assesses the adaptability of these platforms for energy-constrained environments—crucial for real-time applications such as AI-based wearable devices for visually impaired users. The paper offers key insights into different hardware selections, their performance efficiency and future advancements required to boost the capability of wearable AI solutions.
Keywords	Edge AI, Wearable Device, Assistive Technology, Hardware Accelerators, Real-Time AI, Energy Efficiency
Field	Computer Applications
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-05-10
DOI	https://doi.org/10.71097/IJSAT.v16.i2.3006
Short DOI	https://doi.org/g9kc8d

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