Home / Current Issue / Paper 1709741
Real-Time Edge AI: Deploying Efficient Deep Learning Models for On-Device Inference
Subject area: Science,Engineering and Technology · Area of research: Deep Learning
Abstract
Edge AI is transforming the landscape of smart devices by enabling real-time inference on resource-constrained hardware. This paper presents a framework for deploying lightweight deep learning models that strike a balance between accuracy and latency.
How to cite this paper
Luis Madrigal, Ofer Ronen, Leon Chlon "Real-Time Edge AI: Deploying Efficient Deep Learning Models for On-Device Inference" Iconic Research And Engineering Journals Volume 6 Issue 12 2023 Page 1619-1622
Luis Madrigal, Ofer Ronen, Leon Chlon "Real-Time Edge AI: Deploying Efficient Deep Learning Models for On-Device Inference" Iconic Research And Engineering Journals, vol. 6, no. 12, Jul. 2023
Luis Madrigal, Ofer Ronen, Leon Chlon (2023). Real-Time Edge AI: Deploying Efficient Deep Learning Models for On-Device Inference. Iconic Research And Engineering Journals, 6(12).
Luis Madrigal, Ofer Ronen, Leon Chlon "Real-Time Edge AI: Deploying Efficient Deep Learning Models for On-Device Inference" Iconic Research And Engineering Journals, vol. 6, no. 12, Jul. 2023.
@article{1709741,
author = {Luis Madrigal, Ofer Ronen, Leon Chlon},
title = {Real-Time Edge AI: Deploying Efficient Deep Learning Models for On-Device Inference},
journal = {Iconic Research And Engineering Journals},
year = {2023},
volume = {6},
number = {12},
pages = {1619-1622},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709741.pdf},
abstract = {Edge AI is transforming the landscape of smart devices by enabling real-time inference on resource-constrained hardware. This paper presents a framework for deploying lightweight deep learning models that strike a balance between accuracy and latency.},
month = {June},
}