The rapid emergence of low-power embedded devices and modern machine learning (ML)
algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks …

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

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 …

Tiny deep learning on microcontroller units (MCUs) is challenging due to the limited memory
size. We find that the memory bottleneck is due to the imbalanced memory distribution in …

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 …

Abstract With the emerging Tiny Machine Learning (TinyML) inference applications, there is
a growing interest when deploying TinyML models on the low-power Microcontroller Unit …

Spiking neural network models when deployed in dynamic environments, catastrophically
forget previously learned tasks. In this paper, we propose a reconfigurable spiking digital …

Large language models (LLMs) have significantly impacted various domains of our society,
including recent applications in complex fields such as biology and chemistry. These …

This letter presents a novel leading zero counter (LZC) able to efficiently exploits the
hardware resources available within state-of-the-art FPGA devices to achieve high-speed …

In this paper, a new low-precision hardware-software codesign framework is presented, to
optimally select the numerical formats and bit-precision for TinyML models and benchmarks …