95 citations · 151 across the 5 of their papers we have counts for
3 papers · 1 filter
Exploring the limits of Concurrency in ML Training on Google TPUs
Sameer Kumar, James Bradbury, Cliff Young +16
Recent results in language understanding using neural networks have required training hardware of unprecedentedscale, with thousands of chips cooperating on a single training run.…
Scale MLPerf-0.6 models on Google TPU-v3 Pods
Sameer Kumar, Victor Bitorff, Dehao Chen +9
The recent submission of Google TPU-v3 Pods to the industry wide MLPerf v0.6 training benchmark demonstrates the scalability of a suite of industry relevant ML models. MLPerf defin…
Image Classification at Supercomputer Scale
Chris Ying, Sameer Kumar, Dehao Chen +2
Deep learning is extremely computationally intensive, and hardware vendors have responded by building faster accelerators in large clusters. Training deep learning models at petaFL…