Deep Neural Networks (DNNs) have shown significant advantages in many domains, such
as pattern recognition, prediction, and control optimization. The edge computing demand in …

This paper presents the design, implementation, and evaluation of the PyTorch distributed
data parallel module. PyTorch is a widely-adopted scientific computing package used in …

Federated learning is a privacy-preserving machine learning technique that learns a shared
model across decentralized clients. It can alleviate privacy concerns of personal re …

The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …

Deep neural networks (DNNs) are widely used in various AI application domains such as
computer vision, natural language processing, autonomous driving, and bioinformatics. As …

Increasingly stringent data privacy regulations limit the development of person re-
identification (ReID) because person ReID training requires centralizing an enormous …

Modern GPU clusters are designed to support distributed Deep Learning jobs from multiple
tenants concurrently. Each tenant may have varied and dynamic resource demands …

We propose a generalized architecture for the first rapid-single-flux-quantum (RSFQ)
associative memory circuit. The circuit employs hyperdimensional computing (HDC), a …

Modern imaging modalities generate large volumes of medical data that place a heavy
burden on both storage and transmission. Consequently, image data compression is a key …

Crop models are widely used to predict plant growth, water input requirements, and yield.
However, existing models are very complex and require hundreds of variables to perform …