Results 21 to 30 of about 7,124,551 (244)

Incremental On-Device Tiny Machine Learning

, 2020
Tiny Machine Learning (TML) is a novel research area aiming at designing and developing Machine Learning (ML) techniques meant to be executed on Embedded Systems and Internet-of-Things (IoT) units. Such techniques, which take into account the constraints
Disabato S., Roveri M.
core   +1 more source

A Quantitative Review of Automated Neural Search and On-Device Learning for Tiny Devices

Chips, 2023
This paper presents a state-of-the-art review of different approaches for Neural Architecture Search targeting resource-constrained devices such as microcontrollers, as well as the implementations of on-device learning techniques for them.
Danilo Pietro Pau, Prem Kumar Ambrose, Fabrizio Maria Aymone   +2 more
doaj   +1 more source

Deep and wide tiny machine learning [PDF]

, 2022
DOTTORATONegli ultimi decenni e in particolare negli ultimi anni, le soluzioni di Deep Learning sono velocemente diventate lo stato dell'arte in diversi scenari applicativi ``intelligenti''. Gli esempi più noti sono: la classificazione, il rilevamento e
Disabato, Simone
core  

A Cost-Efficient FPGA-Based CNN-Transformer Using Neural ODE

IEEE Access
Transformer has been adopted to image recognition tasks and shown to outperform CNNs and RNNs while it suffers from high training cost and computational complexity.
Ikumi Okubo, Keisuke Sugiura, Hiroki Matsutani   +2 more
doaj   +1 more source

Custom Hardware Inference Accelerator for TensorFlow Lite for Microcontrollers

IEEE Access, 2022
In recent years, the need for the efficient deployment of Neural Networks (NN) on edge devices has been steadily increasing. However, the high computational demand required for Machine Learning (ML) inference on tiny microcontroller-based IoT devices ...
Erez Manor, Shlomo Greenberg
doaj   +1 more source

Learning labelled dependencies in machine translation evaluation [PDF]

, 2009
Recently novel MT evaluation metrics have been presented which go beyond pure string matching, and which correlate better than other existing metrics with human judgements.
He, Yifan, Way, Andy
core   +2 more sources

Navigating the Challenges and Opportunities of Tiny Deep Learning and Tiny Machine Learning in Lung Cancer Identification

مجلة النهرين للعلوم الهندسية
Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens.
Yasir Salam Abdulghafoor, Auns Qusai Al-Neami, Ahmed Faeq Hussein   +2 more
doaj   +1 more source

PhishHaven—An Efficient Real-Time AI Phishing URLs Detection System

IEEE Access, 2020
Different machine learning and deep learning-based approaches have been proposed for designing defensive mechanisms against various phishing attacks. Recently, researchers showed that phishing attacks can be performed by employing a deep neural network ...
Maria Sameen, Kyunghyun Han, Seong Oun Hwang   +2 more
doaj   +1 more source

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

Embedded Machine Learning Using Microcontrollers in Wearable and Ambulatory Systems for Health and Care Applications: A Review

IEEE Access, 2022
The use of machine learning in medical and assistive applications is receiving significant attention thanks to the unique potential it offers to solve complex healthcare problems for which no other solutions had been found. Particularly promising in this
Maha S. Diab, Esther Rodriguez-Villegas
doaj   +1 more source