The rapid growth in miniaturization of low-power embedded devices and advancement in
the optimization of machine learning (ML) algorithms have opened up a new prospect of the …

The research advances concerning the typical architectures of convolutional neural
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …

Abstract Recently, Vision Transformer (ViT) has continuously established new milestones in
the computer vision field, while the high computation and memory cost makes its …

Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

The TinyML paradigm proposes to integrate Machine Learning (ML)-based mechanisms
within small objects powered by Microcontroller Units (MCUs). This paves the way for the …

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 …

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial
intelligence (AI), including computer vision, natural language processing, and speech …

Existing work in continual learning (CL) focuses on mitigating catastrophic forgetting, ie,
model performance deterioration on past tasks when learning a new task. However, the …

Recently, a new trend of exploring sparsity for accelerating neural network training has
emerged, embracing the paradigm of training on the edge. This paper proposes a novel …

Deep Neural Networks (DNNs) have emerged as the core enabler of many major
applications on mobile devices. To achieve high accuracy, DNN models have become …