Industrial Automation (IA) and Artificial Intelligence (AI) need an integrated platform. Due to
the uncertainty of the time required for training or reasoning tasks, it is difficult to ensure the …

Networking has become a well-known performance bottleneck for distributed machine
learning (DML). Although lots of works have focused on accelerating the communication …

Sparse tensors appear frequently in distributed deep learning, either as a direct artifact of
the deep neural network's gradients, or as a result of an explicit sparsification process …

Click-through rate (CTR) prediction is one of the most crucial components in the online
advertising industry. In order to produce a personalized CTR prediction, an industry-level …

Recent works introduce In-Network Aggregation (INA) for distributed training (DT), which
moves the gradient summation into network programmable switches. INA can reduce the …

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

Distributed edge learning collaborates powerful edge devices to train a shared global
model. Since the frequent communication between the server and workers is very …

As deep neural networks (DNNs) grow in complexity and size, the resultant increase in
communication overhead during distributed training has become a significant bottleneck …

摘要以机器学习为代表的人工智能技术需要对海量数据进行处理, 对底层算力要求极高.
分布式机器学习通过将计算任务分布式地部署到多个计算节点来加快模型的训练速度 …

Optimizing performance for Distributed Deep Neural Network (DDNN) training has recently
become increasingly compelling, as the DNN model gets complex and the training dataset …