The advancements in machine learning (ML) opened a new opportunity to bring intelligence
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …

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 Neural Architecture Search (NAS) has shown promising performance in the
automatic design of vision transformers (ViT) exceeding 1G FLOPs. However, designing …

Convolution neural networks (CNNs) have long been dominating the model choice in on-
device intelligent mobile applications. Recently, we are witnessing the fast development of …

Concurrent inference execution on heterogeneous processors is critical to improve the
performance of increasingly heavy deep learning (DL) models. However, available …

Federated learning (FL) becomes a promising machine learning paradigm. The impact of
heterogeneous hardware specifications and dynamic states on the FL process has not yet …

Convolutional neural networks (CNNs) are used in numerous real-world applications such
as vision-based autonomous driving and video content analysis. To run CNN inference on …

Abstract Neural Architecture Search (NAS) has enabled automatic discovery of more
efficient neural network architectures, especially for mobile and embedded vision …

Deep learning applications have been widely adopted on edge devices, to mitigate the
privacy and latency issues of accessing cloud servers. Deciding the number of neurons …

Deep neural networks (DNNs) are widely used in various applications. The accurate and
latency feedback is essential for model design and deployment. In this work, we attempt to …