Results 21 to 30 of about 16,826 (269)
Tiny Machine Learning Environment: Enabling Intelligence on Constrained Devices. [PDF]
, 2023 Running machine learning algorithms (ML) on constrained devices at the extreme edge of the network is problematic due to the computational overhead of ML algorithms, available resources on the embedded platform, and application budget (i.e., real-time requirements, power constraints, etc.).
SAKR, FOUAD
core +6 more sources
Energy-aware Tiny Machine Learning for Sensor-based Hand-washing Recognition [PDF]
, 2023 Tiny wearable devices are nowadays one of the most popular and used devices in everyday life. At the same time, machine learning techniques have reached a level of maturity such that they can be used in the most varied fields.
Emanuele Lattanzi, Lorenzo Calisti
core +1 more source
Measuring Comfort Behaviours in Laying Hens Using Deep-Learning Tools
Animals, 2022 Image analysis using machine learning (ML) algorithms could provide a measure of animal welfare by measuring comfort behaviours and undesired behaviours.
Marco Sozzi +8 more
doaj +1 more source
TIFeD: a Tiny Integer-based Federated learning algorithm with Direct feedback alignment [PDF]
, 2023 Training machine and deep learning models directly on extremely resource-constrained devices is the next challenge in the field of tiny machine learning.
Roveri, Manuel +2 more
core +1 more source
MiCrowd: Vision-Based Deep Crowd Counting on MCU
Sensors, 2023 Microcontrollers (MCUs) have been deployed on numerous IoT devices due to their compact sizes and low costs. MCUs are capable of capturing sensor data and processing them. However, due to their low computational power, applications processing sensor data
Sungwook Son +5 more
doaj +1 more source
Machine learning for microalgae detection and utilization
Frontiers in Marine Science, 2022 Microalgae are essential parts of marine ecology, and they play a key role in species balance. Microalgae also have significant economic value. However, microalgae are too tiny, and there are many different kinds of microalgae in a single drop of ...
Hongwei Ning, Rui Li, Teng Zhou
doaj +1 more source
Edge Impulse: An MLOps Platform for Tiny Machine Learning
CoRR, 2022 Edge Impulse is a cloud-based machine learning operations (MLOps) platform for developing embedded and edge ML (TinyML) systems that can be deployed to a wide range of hardware targets. Current TinyML workflows are plagued by fragmented software stacks and heterogeneous deployment hardware, making ML model optimizations difficult and unportable.
Colby R. Banbury +15 more
openaire +3 more sources
An 8-bit Single Perceptron Processing Unit for Tiny Machine Learning Applications
IEEE Access, 2023 We present a tiny MultiLayer Perceptron (MLP) accelerator named Single Perceptron Linear Vector Processor (SPLVP) that aims at extending the capabilities of limited resources MCUs, enabling inference time speedup and main CPU off-load.
Marco Crepaldi +2 more
doaj +1 more source
Solving time-varying maze with deep reinforcement learning for tiny devices [PDF]
, 2022 LAUREA MAGISTRALENell'ambito del ‘Tiny Machine Learning’ (Tiny ML), l'adozione del ‘Deep Reinforcement Learning’ (DRL) è stata fortemente limitata a causa degli onerosi costi computazionali che tali algoritmi richiedono.
Colella, Stefano
core
A review on TinyML: State-of-the-art and prospects
Journal of King Saud University: Computer and Information Sciences, 2022 Machine learning has become an indispensable part of the existing technological domain. Edge computing and Internet of Things (IoT) together presents a new opportunity to imply machine learning techniques at the resource constrained embedded devices at ...
Partha Pratim Ray
doaj +1 more source