However, these new AI workstations have computational power and architecture ... to other areas: Convolutional Neural Networks (CNNs) for computer vision and image analysis; Large Action Models (LAMs) ...

The recent launch of TensorFlow GNN offers a streamlined way to design ... example social networks, recommendation systems, physics simulations, and so on. GNNs extend the foundational ideas of ...

Existing predictor-based NAS methods often rely on graph convolutional ... group network (VGG), ResNet, DenseNet, and MobileNet have achieved high performance across various tasks, but design them is ...

The backbone of our framework consists of a 30-layer 3D fully convolutional neural network (CNN). The architecture contains skip connections ... By utilizing a light-weight design with low-depth ...

In this paper, we present a run-time configurable convolutional neural network (CNN) inference ASIC design for low-latency edge machine learning. By implementing a 5-stage pipelined CNN inference ...

Abstract: Graph convolutional networks (GCNs ... GCNs to take advantage of fast near-data processing of the 3D-stacked CIM architecture. This article presents GCIM, a software–hardware co-design ...

In this section, we present the original GNN and its variant models, including graph convolutional ... of GNNs on the molecular 3D structure may be a future direction that has been neglected ...