Machine learning is widely used to solve networking challenges, ranging from traffic
classification and anomaly detection to network configuration. However, machine learning …

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

Training machine learning models in parallel is an increasingly important workload. We
accelerate distributed parallel training by designing a communication primitive that uses a …

Exploration is critical for good results in deep reinforcement learning and has attracted much
attention. However, existing multi-agent deep reinforcement learning algorithms still use …

Machine learning is currently driving a technological and societal revolution. While
programmable switches have been proven to be useful for in-network computing, machine …

Efficient collective communication is crucial to parallel-computing applications such as
distributed training of large-scale recommendation systems and natural language …

The rat race between user-generated data and data-processing systems is currently won by
data. The increased use of machine learning leads to further increase in processing …

In this paper, we introduce the Tensor Streaming Processor (TSP) architecture, a functionally-
sliced microarchitecture with memory units interleaved with vector and matrix deep learning …

Deploying accurate machine learning algorithms into a high-throughput networking
environment is a challenging task. On the one hand, machine learning has proved itself …

Intrusion detection through classifying incoming packets is a crucial functionality at the
network edge, requiring accuracy, efficiency and scalability at the same time, introducing a …