19 citations · 44 across the 4 of their papers we have counts for
6 papers
Machine Learning (ML) In a 5G Standalone (SA) Self Organizing Network (SON)
Srinivasan Sridharan
Machine learning (ML) is included in Self-organizing Networks (SONs) that are key drivers for enhancing the Operations, Administration, and Maintenance (OAM) activities. It is incl…
Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems
Maxim Naumov, John Kim, Dheevatsa Mudigere +12
Large-scale training is important to ensure high performance and accuracy of machine-learning models. At Facebook we use many different models, including computer vision, video and…
Automatic Model Parallelism for Deep Neural Networks with Compiler and Hardware Support
Sanket Tavarageri, Srinivas Sridharan, Bharat Kaul
The deep neural networks (DNNs) have been enormously successful in tasks that were hitherto in the human-only realm such as image recognition, and language translation. Owing to th…
On Scale-out Deep Learning Training for Cloud and HPC
Srinivas Sridharan, Karthikeyan Vaidyanathan, Dhiraj Kalamkar +8
The exponential growth in use of large deep neural networks has accelerated the need for training these deep neural networks in hours or even minutes. This can only be achieved thr…
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data
Thorsten Kurth, Jian Zhang, Nadathur Satish +12
This paper presents the first, 15-PetaFLOP Deep Learning system for solving scientific pattern classification problems on contemporary HPC architectures. We develop supervised conv…
Distributed Deep Learning Using Synchronous Stochastic Gradient Descent
Dipankar Das, Sasikanth Avancha, Dheevatsa Mudigere +5
We design and implement a distributed multinode synchronous SGD algorithm, without altering hyper parameters, or compressing data, or altering algorithmic behavior. We perform a de…