Intelligence at the extreme edge: A survey on reformable tinyml
Machine Learning (TinyML) is an upsurging research field that proposes to democratize the
use of Machine Learning and Deep Learning on highly energy-efficient frugal …
use of Machine Learning and Deep Learning on highly energy-efficient frugal …
A survey on approximate edge AI for energy efficient autonomous driving services
Autonomous driving services depends on active sensing from modules such as camera,
LiDAR, radar, and communication units. Traditionally, these modules process the sensed …
LiDAR, radar, and communication units. Traditionally, these modules process the sensed …
An adaptable and unsupervised TinyML anomaly detection system for extreme industrial environments
Industrial assets often feature multiple sensing devices to keep track of their status by
monitoring certain physical parameters. These readings can be analyzed with machine …
monitoring certain physical parameters. These readings can be analyzed with machine …
Edge mlops: An automation framework for aiot applications
E Raj, D Buffoni, M Westerlund… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Artificial Intelligence of Things (AIoT) is the combination of artificial intelligence (AI)
technologies with the Internet of Things (IoT) infrastructure to achieve more efficient IoT …
technologies with the Internet of Things (IoT) infrastructure to achieve more efficient IoT …
Ml-mcu: A framework to train ml classifiers on mcu-based iot edge devices
The majority of IoT edge devices are embedded systems with a tiny microcontroller unit
(MCU), which acts as its brain. When users want their edge devices to continuously improve …
(MCU), which acts as its brain. When users want their edge devices to continuously improve …
[HTML][HTML] Internet of Intelligent Things: A convergence of embedded systems, edge computing and machine learning
This article comprehensively reviews the emerging concept of Internet of Intelligent Things
(IoIT), adopting an integrated perspective centred on the areas of embedded systems, edge …
(IoIT), adopting an integrated perspective centred on the areas of embedded systems, edge …
Train++: An incremental ml model training algorithm to create self-learning iot devices
The majority of Internet of Things (IoT) devices are tiny embedded systems with a micro-
controller unit (MCU) as its brain. The memory footprint (SRAM, Flash, and EEPROM) of …
controller unit (MCU) as its brain. The memory footprint (SRAM, Flash, and EEPROM) of …
RCE-NN: a five-stage pipeline to execute neural networks (cnns) on resource-constrained iot edge devices
Microcontroller Units (MCUs) in edge devices are resource constrained due to their limited
memory footprint, fewer computation cores, and low clock speeds. These limitations …
memory footprint, fewer computation cores, and low clock speeds. These limitations …
[HTML][HTML] An evaluation methodology to determine the actual limitations of a tinyml-based solution
Abstract Tiny Machine Learning (TinyML) is an expanding research area based on pushing
intelligence to the edge and bringing machine learning techniques to very small devices and …
intelligence to the edge and bringing machine learning techniques to very small devices and …
Edge2Analysis: a novel AIoT platform for atrial fibrillation recognition and detection
J Chen, Y Zheng, Y Liang, Z Zhan… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Atrial fibrillation (AF) is a serious medical condition of the heart potentially leading to stroke,
which can be diagnosed by analyzing electrocardiograms (ECG). Technologies of Artificial …
which can be diagnosed by analyzing electrocardiograms (ECG). Technologies of Artificial …