66 citations · 92 across the 3 of their papers we have counts for
5 papers
High-Performance Deep Learning via a Single Building Block
Evangelos Georganas, Kunal Banerjee, Dhiraj Kalamkar +6
Deep learning (DL) is one of the most prominent branches of machine learning. Due to the immense computational cost of DL workloads, industry and academia have developed DL librari…
A Study of BFLOAT16 for Deep Learning Training
Dhiraj Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi +16
This paper presents the first comprehensive empirical study demonstrating the efficacy of the Brain Floating Point (BFLOAT16) half-precision format for Deep Learning training acros…
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…
RAIL: Risk-Averse Imitation Learning
Anirban Santara, Abhishek Naik, Balaraman Ravindran +4
Imitation learning algorithms learn viable policies by imitating an expert's behavior when reward signals are not available. Generative Adversarial Imitation Learning (GAIL) is a s…
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…