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

Recent In-Network Aggregation (INA) solutions offload the all-reduce operation onto network
switches to accelerate and scale distributed training (DT). On end hosts, these solutions …

Programmable switches are recently used for accelerating data-intensive distributed
applications. Some computational tasks, traditionally performed on servers in data centers …

In this paper we present a new approach for distributed DBMSs called P4DB, that uses a
programmable switch to accelerate OLTP workloads. The main idea of P4DB is that it …

With the rapid development of deep learning, training Big Models (BMs) for multiple
downstream tasks becomes a popular paradigm. Researchers have achieved various …

As the scale of distributed training grows, communication becomes a bottleneck. To
accelerate the communication, recent works introduce In-Network Aggregation (INA), which …

Experience replay is an essential component in deep reinforcement learning (DRL), which
stores the experiences and generates experiences for the agent to learn in real time …

Depression is regarded as an important issue because it is the largest cause of disability
around the world and a major factor in the formation of serious medical conditions, which …

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

We present NetReduce, a novel RDMA-compatible in-network reduction architecture to
accelerate distributed DNN training. Compared to existing designs, NetReduce maintains a …