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 …

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 …

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 …

Tiny machine learning on IoT devices based on microcontroller units (MCUs) enables
various real-world applications (eg, keyword spotting, anomaly detection). However …

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 …

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 …

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 …

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 …

Tiny Machine Learning (TinyML), a rapidly evolving edge computing concept that links
embedded systems (hardware and software) and machine learning, with the purpose of …

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 …