Results 31 to 40 of about 16,826 (269)
TinyRCE: Forward Learning Under Tiny Constraints [PDF]
, 2023 The challenge posed by on-tiny-devices learning targeting ultra-low power devices has recently attracted several machine learning researchers. A typical on-device model learning session processes real time streams of data produced by heterogeneous ...
Pau, Danilo +3 more
core +1 more source
Micromachines, 2022 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, which render them intelligent and able to make decisions.
Norah N. Alajlan, Dina M. Ibrahim
doaj +1 more source
The synergy of complex event processing and tiny machine learning in industrial IoT [PDF]
Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems, 2021 Accepted by The 15th ACM International Conference on Distributed and Event-based Systems (DEBS ...
Haoyu Ren +2 more
openaire +2 more sources
DeepEdge: A Novel Appliance Identification Edge Platform for Data Gathering, Capturing and Labeling
Sensors, 2022 With the development of the Internet of Things for smart grid, the requirement for appliance monitoring has become an important topic. The first and most important step in appliance monitoring is to identify the type of appliance.
Zilin Wang +5 more
doaj +1 more source
Towards Open Ended Learning: Budgets, Model Selection, and Representation [PDF]
, 2011 Biological organisms learn to recognize visual categories continuously over the course of their lifetimes. This impressive capability allows them to adapt to new circumstances as they arise, and to flexibly incorporate new object categories as they are ...
Gomes, Ryan Geoffrey
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 +2 more
doaj +1 more source
A Cost-Efficient FPGA-Based CNN-Transformer Using Neural ODE
IEEE AccessTransformer 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 +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
How to Manage Tiny Machine Learning at Scale: An Industrial Perspective
CoRR, 2022 Accepted by The 2022 tinyML Research ...
Haoyu Ren +2 more
openaire +2 more sources
Computers, 2023 The technological step towards sensors’ miniaturization, low-cost platforms, and evolved communication paradigms is rapidly moving the monitoring and computation tasks to the edge, causing the joint use of the Internet of Things (IoT) and machine ...
Michele Vitelli +5 more
doaj +1 more source