Real-Time Edge AI: Deploying Efficient Deep Learning Models for On-Device Inference
  • Author(s): Luis Madrigal; Ofer Ronen; Leon Chlon
  • Paper ID: 1709741
  • Page: 1619-1622
  • Published Date: 30-06-2023
  • Published In: Iconic Research And Engineering Journals
  • Publisher: IRE Journals
  • e-ISSN: 2456-8880
  • Volume/Issue: Volume 6 Issue 12 June-2023
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.

Citations

IRE Journals:
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

IEEE:
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, Jun. 2023

APA:
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).

MLA:
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, Jun. 2023.

BibTeX

@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}
}