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 …

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 …

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

Recent advances in state-of-the-art DNN architecture design have been moving toward
Transformer models. These models achieve superior accuracy across a wide range of …

Neural Radiance Field (NeRF) based rendering has attracted growing attention thanks to its
state-of-the-art (SOTA) rendering quality and wide applications in Augmented and Virtual …

Emergency situations encompassing natural and human-made disasters, as well as their
cascading effects, pose serious threats to society at large. Machine learning (ML) algorithms …

Whether it is used for predictive maintenance, intrusion detection or surveillance, on-device
anomaly detection is a very valuable functionality in sensor and Internet-of-things (IoT) …

Running multiple deep neural networks (DNNs) in parallel has become an emerging
workload in both edge devices, such as mobile phones where multiple tasks serve a single …

Many data have an underlying dependence on spatial location; it may be weather on the
Earth, a simulation on a mesh, or a registered image. Yet this feature is rarely taken …

Deep learning accelerators are important tools for feeding the growing demand for deep
learning applications. The automated design of such accelerators—which is important for …