171 citations · 186 across the 4 of their papers we have counts for
9 papers
Using Python for Model Inference in Deep Learning
Zachary DeVito, Jason Ansel, Will Constable +3
Python has become the de-facto language for training deep neural networks, coupling a large suite of scientific computing libraries with efficient libraries for tensor computation…
Understanding Training Efficiency of Deep Learning Recommendation Models at Scale
Bilge Acun, Matthew Murphy, Xiaodong Wang +3
The use of GPUs has proliferated for machine learning workflows and is now considered mainstream for many deep learning models. Meanwhile, when training state-of-the-art personal r…
RecNMP: Accelerating Personalized Recommendation with Near-Memory Processing
Liu Ke, Udit Gupta, Carole-Jean Wu +18
Personalized recommendation systems leverage deep learning models and account for the majority of data center AI cycles. Their performance is dominated by memory-bound sparse embed…
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…
Exploiting Parallelism Opportunities with Deep Learning Frameworks
Yu Emma Wang, Carole-Jean Wu, Xiaodong Wang +2
State-of-the-art machine learning frameworks support a wide variety of design features to enable a flexible machine learning programming interface and to ease the programmability b…
The Architectural Implications of Facebook's DNN-based Personalized Recommendation
Udit Gupta, Carole-Jean Wu, Xiaodong Wang +12
The widespread application of deep learning has changed the landscape of computation in the data center. In particular, personalized recommendation for content ranking is now large…