19 citations · 24 across the 5 of their papers we have counts for
5 papers
FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search
Jordan Dotzel, Gang Wu, Andrew Li +9
Quantization has become a mainstream compression technique for reducing model size, computational requirements, and energy consumption for modern deep neural networks (DNNs). With…
RETROSPECTIVE: Corona: System Implications of Emerging Nanophotonic Technology
Dana Vantrease, Robert Schreiber, Matteo Monchiero +7
The 2008 Corona effort was inspired by a pressing need for more of everything, as demanded by the salient problems of the day. Dennard scaling was no longer in effect. A lot of com…
TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings
Norman P. Jouppi, George Kurian, Sheng Li +11
In response to innovations in machine learning (ML) models, production workloads changed radically and rapidly. TPU v4 is the fifth Google domain specific architecture (DSA) and it…
Searching for Fast Model Families on Datacenter Accelerators
Sheng Li, Mingxing Tan, Ruoming Pang +4
Neural Architecture Search (NAS), together with model scaling, has shown remarkable progress in designing high accuracy and fast convolutional architecture families. However, as ne…
Highly Available Data Parallel ML training on Mesh Networks
Sameer Kumar, Norm Jouppi
Data parallel ML models can take several days or weeks to train on several accelerators. The long duration of training relies on the cluster of resources to be available for the jo…