Recent years have seen a phenomenal rise in the performance and applications of
transformer neural networks. The family of transformer networks, including Bidirectional …

The growth of the Machine-Learning-As-A-Service (MLaaS) market has highlighted clients'
data privacy and security issues. Private inference (PI) techniques using cryptographic …

Abstract The growth of Graph Convolution Network (GCN) model sizes has revolutionized
numerous applications, surpassing human performance in areas such as personal …

This paper addresses the challenges in accurate and real-time traffic congestion prediction
under uncertainty by proposing Ising-Traffic, a dual-model Ising-based traffic prediction …

Graph Convolutional Networks (GCNs) are pivotal in extracting latent information from graph
data across various domains, yet their acceleration on mainstream GPUs is challenged by …

As real-world graphs expand in size, larger GNN models with billions of parameters are
deployed. High parameter count in such models makes training and inference on graphs …

With the advancement of computer technology, transformer models have been applied to the
field of computer vision (CV) after their success in natural language processing (NLP). In …

Recent advancements in large language models (LLMs) boasting billions of parameters
have generated a significant demand for efficient deployment in inference workloads. While …

In the acceleration of deep neural network training, the graphics processing unit (GPU) has
become the mainstream platform. GPUs face substantial challenges on Graph Neural …

The proliferation of deep learning (DL) has led to the emergence of privacy and security
concerns. To address these issues, secure Two-party computation (2PC) has been …