Edge machine learning: Enabling smart internet of things applications
Machine learning has traditionally been solely performed on servers and high-performance
machines. However, advances in chip technology have given us miniature libraries that fit in …
machines. However, advances in chip technology have given us miniature libraries that fit in …
Deep learning for the internet of things
How can the advantages of deep learning be brought to the emerging world of embedded
IoT devices? The authors discuss several core challenges in embedded and mobile deep …
IoT devices? The authors discuss several core challenges in embedded and mobile deep …
Edge machine learning for ai-enabled iot devices: A review
In a few years, the world will be populated by billions of connected devices that will be
placed in our homes, cities, vehicles, and industries. Devices with limited resources will …
placed in our homes, cities, vehicles, and industries. Devices with limited resources will …
On-device learning systems for edge intelligence: A software and hardware synergy perspective
Modern machine learning (ML) applications are often deployed in the cloud environment to
exploit the computational power of clusters. However, this in-cloud computing scheme …
exploit the computational power of clusters. However, this in-cloud computing scheme …
The role of machine learning techniques in internet of things-based cloud applications
S Mishra, AK Tyagi - Artificial intelligence-based internet of things systems, 2022 - Springer
Abstract Today's Machine Learning (ML) in a blend with Internet of Things (IoT)-based cloud
applications plays a significant role in our everyday life. As indicated by Gartner's recent …
applications plays a significant role in our everyday life. As indicated by Gartner's recent …
A survey of on-device machine learning: An algorithms and learning theory perspective
The predominant paradigm for using machine learning models on a device is to train a
model in the cloud and perform inference using the trained model on the device. However …
model in the cloud and perform inference using the trained model on the device. However …
TinyML: Enabling of inference deep learning models on ultra-low-power IoT edge devices for AI applications
NN Alajlan, DM Ibrahim - Micromachines, 2022 - mdpi.com
Recently, the Internet of Things (IoT) has gained a lot of attention, since IoT devices are
placed in various fields. Many of these devices are based on machine learning (ML) models …
placed in various fields. Many of these devices are based on machine learning (ML) models …
Edge2train: A framework to train machine learning models (svms) on resource-constrained iot edge devices
In recent years, ML (Machine Learning) models that have been trained in data centers can
often be deployed for use on edge devices. When the model deployed on these devices …
often be deployed for use on edge devices. When the model deployed on these devices …
Integration of deep learning into the iot: A survey of techniques and challenges for real-world applications
The internet of things (IoT) has emerged as a pivotal technological paradigm facilitating
interconnected and intelligent devices across multifarious domains. The proliferation of IoT …
interconnected and intelligent devices across multifarious domains. The proliferation of IoT …
Enabling deep learning on IoT devices
Deep learning can enable Internet of Things (IoT) devices to interpret unstructured
multimedia data and intelligently react to both user and environmental events but has …
multimedia data and intelligently react to both user and environmental events but has …