Programmable data plane technologies enable the systematic reconfiguration of the low-
level processing steps applied to network packets and are key drivers toward realizing the …

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 …

Distributed deep neural network training (DT) systems are widely deployed in clusters where
the network is shared across multiple tenants, ie, multiple DT jobs. Each DT job computes …

Programmable data plane hardware creates new opportunities for infusing intelligence into
the network. This raises a fundamental question: what kinds of computation should be …

This paper explores the possibility of implementing the widely deployed Paxos consensus
protocol in network devices. We present two different approaches:(i) a detailed design …

The emergence of programmable network devices and the increasing data traffic of
datacenters motivate the idea of in-network computation. By offloading compute operations …

Increased system size and a greater reliance on utilizing system parallelism to achieve
computational needs, requires innovative system architectures to meet the simulation …

We present PANAMA, a network architecture for machine learning (ML) workloads on
shared clusters where a variety of training jobs co-exist. PANAMA consists of two key …

Middleboxes that process confidential data cannot be securely deployed in untrusted cloud
environments. To securely outsource middleboxes to the cloud, state-of-the-art systems …