Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …

The demand for machine learning (ML) has increased significantly in recent decades,
enabling several applications, such as speech recognition, computer vision, and …

Aggregation applications are widely deployed in data centers, such as distributed machine
learning and MapReduce-like framework. These applications typically have large …

The surging scale of distributed training (DT) incurs significant communication overhead in
datacenters, while a promising solution is in-network aggregation (INA). It leverages …

The surging scale of distributed training (DT) incurs significant communication overhead in
datacenters, while a promising solution is in-network aggregation (INA). It leverages …

Deep Neural Network (DNN) is a critical component of a wide range of applications.
However, with the rapid growth of the training dataset and model size, communication …

By offloading partial of the aggregation computation from the logical central parameter
servers to network devices like programmable switches, In-Network Aggregation (INA) is a …

Large-scale datacenter networks are increasingly using in-network aggregation (INA) and
remote direct memory access (RDMA) techniques to accelerate deep neural network (DNN) …

In-network aggregation facilitates accelerated distributed deep learning by utilizing a
programmable switch to aggregate gradient packets. However, a straggler problem should …

Achieving high‐performance aggregation is essential to scaling data‐parallel distributed
machine learning (ML) training. Recent research in in‐network computing has shown that …