222 citations · 467 across the 10 of their papers we have counts for
10 papers
Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput
Arissa Wongpanich, Tayo Oguntebi, Jose Baiocchi Paredes +6
Recent years have seen the emergence of machine learning (ML) workloads deployed in warehouse-scale computing (WSC) settings, also known as ML fleets. As the computational demands…
Deep Learning Based Model for Breast Cancer Subtype Classification
Sheetal Rajpal, Virendra Kumar, Manoj Agarwal +1
Breast cancer has long been a prominent cause of mortality among women. Diagnosis, therapy, and prognosis are now possible, thanks to the availability of RNA sequencing tools capab…
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.…
Generalized Multi-view Shared Subspace Learning using View Bootstrapping
Krishna Somandepalli, Shrikanth Narayanan
A key objective in multi-view learning is to model the information common to multiple parallel views of a class of objects/events to improve downstream learning tasks. In this cont…
Quantized deep learning models on low-power edge devices for robotic systems
Anugraha Sinha, Naveen Kumar, Murukesh Mohanan +4
In this work, we present a quantized deep neural network deployed on a low-power edge device, inferring learned motor-movements of a suspended robot in a defined space. This serves…
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…