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, 6(12)
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 9:
Issue 1Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12