2.2k citations · 5.1k across the 21 of their papers we have counts for
5 papers · 1 filter
MLPerf Inference Benchmark
Vijay Janapa Reddi, Christine Cheng, David Kanter +44
Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organization…
MLPerf Training Benchmark
Peter Mattson, Christine Cheng, Cody Coleman +34
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But M…
Beyond Human-Level Accuracy: Computational Challenges in Deep Learning
Joel Hestness, Newsha Ardalani, Greg Diamos
Deep learning (DL) research yields accuracy and product improvements from both model architecture changes and scale: larger data sets and models, and more computation. For hardware…
EPNAS: Efficient Progressive Neural Architecture Search
Yanqi Zhou, Peng Wang, Sercan Arik +4
In this paper, we propose Efficient Progressive Neural Architecture Search (EPNAS), a neural architecture search (NAS) that efficiently handles large search space through a novel p…
Coloring Big Graphs with AlphaGoZero
Jiayi Huang, Mostofa Patwary, Gregory Diamos
We show that recent innovations in deep reinforcement learning can effectively color very large graphs -- a well-known NP-hard problem with clear commercial applications. Because t…