Accelerating Edge Intelligence: Challenges and Future Directions in Hardware-Driven Machine Learning
Year: 2024
Type: conference-paper
Abstract: The joining of hardware speed-up for machine learning (ML) in small devices shows a very important step in technology growth.This ability to run complicated ML models on low-power systems opens up many uses, like Internet of Things (IoT) gadgets, robots and different edge computing tasks.This paper studies the present condition of hardware speed-up for ML in small devices, clarifies difficulties and looks at future paths for progress.
Institution: Democritus University of Thrace
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: Trakya Üniversitesi
OpenAlex ID: W4410824731