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 emerging field of artificial intelligence of things (AIoT, AI+ IoT) is driven by the
widespread use of intelligent infrastructures and the impressive success of deep learning …

Abstract Machine learning models are increasingly being scaled in both sequence length
and model dimension to reach longer contexts and better performance. However, existing …

Large Language Models (LLMs) such as GPTs and LLaMa have ushered in a revolution in
machine intelligence, owing to their exceptional capabilities in a wide range of machine …

Large Language Models (LLMs) are transforming the landscape of mobile intelligence.
Federated Learning (FL), a method to preserve user data privacy, is often employed in fine …

This paper proposes Mandheling, the first system that enables highly resource-efficient on-
device training by orchestrating mixed-precision training with on-chip Digital Signal …

Large Language Models (LLMs) are transforming the landscape of mobile intelligence.
Federated Learning (FL), a method to preserve user data privacy, is often employed in fine …

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 …

Convolution models with long filters have demonstrated state-of-the-art reasoning abilities in
many long-sequence tasks but lag behind the most optimized Transformers in wall-clock …

Continual learning (CL) trains NN models incrementally from a continuous stream of tasks.
To remember previously learned knowledge, prior studies store old samples over a memory …