2.1k citations · 2.4k across the 6 of their papers we have counts for
6 papers
PaLM 2 Technical Report
Rohan Anil, Andrew M. Dai, Orhan Firat +125
We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 i…
PaLM: Scaling Language Modeling with Pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin +64
Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of…
Pathways: Asynchronous Distributed Dataflow for ML
Paul Barham, Aakanksha Chowdhery, Jeff Dean +13
We present the design of a new large scale orchestration layer for accelerators. Our system, Pathways, is explicitly designed to enable exploration of new systems and ML research i…
Scaling Video Analytics on Constrained Edge Nodes
Christopher Canel, Thomas Kim, Giulio Zhou +5
As video camera deployments continue to grow, the need to process large volumes of real-time data strains wide area network infrastructure. When per-camera bandwidth is limited, it…
3LC: Lightweight and Effective Traffic Compression for Distributed Machine Learning
Hyeontaek Lim, David G. Andersen, Michael Kaminsky
The performance and efficiency of distributed machine learning (ML) depends significantly on how long it takes for nodes to exchange state changes. Overly-aggressive attempts to re…
NetMemex: Providing Full-Fidelity Traffic Archival
Hyeontaek Lim, Vyas Sekar, Yoshihisa Abe +1
NetMemex explores efficient network traffic archival without any loss of information. Unlike NetFlow-like aggregation, NetMemex allows retrieving the entire packet data including f…