424 citations · 1.1k across the 8 of their papers we have counts for
7 papers · 1 filter
Data Engineering for Everyone
Vijay Janapa Reddi, Greg Diamos, Pete Warden +2
Data engineering is one of the fastest-growing fields within machine learning (ML). As ML becomes more common, the appetite for data grows more ravenous. But ML requires more data…
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…
Deep Learning Scaling is Predictable, Empirically
Joel Hestness, Sharan Narang, Newsha Ardalani +6
Deep learning (DL) creates impactful advances following a virtuous recipe: model architecture search, creating large training data sets, and scaling computation. It is widely belie…