3 citations · 5 across the 2 of their papers we have counts for
3 papers · 1 filter
Deep Learning Models on CPUs: A Methodology for Efficient Training
Quchen Fu, Ramesh Chukka, Keith Achorn +5
GPUs have been favored for training deep learning models due to their highly parallelized architecture. As a result, most studies on training optimization focus on GPUs. There is o…
MLPerf Mobile Inference Benchmark
Vijay Janapa Reddi, David Kanter, Peter Mattson +24
This paper presents the first industry-standard open-source machine learning (ML) benchmark to allow perfor mance and accuracy evaluation of mobile devices with different AI chips…
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…