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 …

High throughput serving of large language models (LLMs) requires batching sufficiently
many requests at a time. However, existing systems struggle because the key-value cache …

On-device training enables the model to adapt to new data collected from the sensors by
fine-tuning a pre-trained model. Users can benefit from customized AI models without having …

The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …

On-device learning and efficient fine-tuning enable continuous and privacy-preserving
customization (eg, locally fine-tuning large language models on personalized data) …

Edge artificial intelligence (AI) is an innovative computing paradigm that aims to shift the
training and inference of machine learning models to the edge of the network. This paradigm …

On-device training is essential for user personalisation and privacy. With the pervasiveness
of IoT devices and microcontroller units (MCU), this task becomes more challenging due to …

The size of deep neural networks has grown exponentially in recent years. Unfortunately,
hardware devices have not kept pace with the rapidly increasing memory requirements. To …

The recent breakthroughs in machine learning (ML) and deep learning (DL) have catalyzed
the design and development of various intelligent systems over wide application domains …

Large language models (LLMs) have unlocked a plethora of powerful applications at the
network edge, such as intelligent personal assistants. Data privacy and security concerns …