Putting ai on diet: Tinyml and efficient deep learning
S Han - 2021 International Symposium on VLSI Design …, 2021 - ieeexplore.ieee.org
Summary form only given, as follows. A complete record of the panel discussion was not
made available for publication as part of the conference proceedings. Deep leaning …
made available for publication as part of the conference proceedings. Deep leaning …
A machine learning-oriented survey on tiny machine learning
The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of
Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware …
Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware …
[BOOK][B] Machine Learning on Commodity Tiny Devices: Theory and Practice
This book aims at the tiny machine learning (TinyML) software and hardware synergy for
edge intelligence applications. It presents on-device learning techniques covering model …
edge intelligence applications. It presents on-device learning techniques covering model …
Efficient Algorithms and Systems for Tiny Deep Learning
J Lin - 2021 - dspace.mit.edu
Tiny machine learning on IoT devices based on microcontroller units (MCUs) enables
various real-world applications (eg, keyword spotting, anomaly detection). However …
various real-world applications (eg, keyword spotting, anomaly detection). However …
A tinymlaas ecosystem for machine learning in iot: Overview and research challenges
H Doyu, R Morabito… - … Symposium on VLSI …, 2021 - ieeexplore.ieee.org
Tiny Machine Learning (TinyML) is an emerging concept that concerns the execution of ML
tasks on very constrained IoT devices. Although TinyML has generated a strong R&D …
tasks on very constrained IoT devices. Although TinyML has generated a strong R&D …
Tiny machine learning: progress and futures [feature]
Tiny machine learning (TinyML) is a new frontier of machine learning. By squeezing deep
learning models into billions of IoT devices and microcontrollers (MCUs), we expand the …
learning models into billions of IoT devices and microcontrollers (MCUs), we expand the …
Realising the power of edge intelligence: addressing the challenges in AI and tinyML applications for edge computing
The edge computing paradigm has become increasingly popular due to its benefits over
cloud computing, particularly in the context of AI and IoT applications. Its harmonising with AI …
cloud computing, particularly in the context of AI and IoT applications. Its harmonising with AI …
TinyML Applications, Research Challenges, and Future Research Directions
In recent times, both the academic and industrial sectors have developed a greater interest
in artificial intelligence (AI) and machine learning (ML). Conventional ML approaches …
in artificial intelligence (AI) and machine learning (ML). Conventional ML approaches …
TinyML: A systematic review and synthesis of existing research
Tiny Machine Learning (TinyML), a rapidly evolving edge computing concept that links
embedded systems (hardware and software) and machine learning, with the purpose of …
embedded systems (hardware and software) and machine learning, with the purpose of …
Tiny Machine Learning for Resource‐Constrained Microcontrollers
R Immonen, T Hämäläinen - Journal of Sensors, 2022 - Wiley Online Library
We use 250 billion microcontrollers daily in electronic devices that are capable of running
machine learning models inside them. Unfortunately, most of these microcontrollers are …
machine learning models inside them. Unfortunately, most of these microcontrollers are …