148 citations · 557 across the 22 of their papers we have counts for
11 papers · 1 filter
Scalable Pretraining of Large Mixture of Experts Language Models on Aurora Super Computer
Dharma Teja Vooturi, Dhiraj Kalamkar, Dipankar Das +1
Pretraining Large Language Models (LLMs) from scratch requires massive amount of compute. Aurora super computer is an ExaScale machine with 127,488 Intel PVC (Ponte Vechio) GPU til…
AUTOSPARSE: Towards Automated Sparse Training of Deep Neural Networks
Abhisek Kundu, Naveen K. Mellempudi, Dharma Teja Vooturi +2
Sparse training is emerging as a promising avenue for reducing the computational cost of training neural networks. Several recent studies have proposed pruning methods using learna…
Efficient and Generic 1D Dilated Convolution Layer for Deep Learning
Narendra Chaudhary, Sanchit Misra, Dhiraj Kalamkar +5
Convolutional neural networks (CNNs) have found many applications in tasks involving two-dimensional (2D) data, such as image classification and image processing. Therefore, 2D con…
SEERL: Sample Efficient Ensemble Reinforcement Learning
Rohan Saphal, Balaraman Ravindran, Dheevatsa Mudigere +2
Ensemble learning is a very prevalent method employed in machine learning. The relative success of ensemble methods is attributed to their ability to tackle a wide range of instanc…
K-TanH: Efficient TanH For Deep Learning
Abhisek Kundu, Alex Heinecke, Dhiraj Kalamkar +7
We propose K-TanH, a novel, highly accurate, hardware efficient approximation of popular activation function TanH for Deep Learning. K-TanH consists of parameterized low-precision…
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…
Mixed Precision Training With 8-bit Floating Point
Naveen Mellempudi, Sudarshan Srinivasan, Dipankar Das +1
Reduced precision computation for deep neural networks is one of the key areas addressing the widening compute gap driven by an exponential growth in model size. In recent years, d…
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers
Apoorv Vyas, Nataraj Jammalamadaka, Xia Zhu +3
As deep learning methods form a critical part in commercially important applications such as autonomous driving and medical diagnostics, it is important to reliably detect out-of-d…
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…
Ternary Neural Networks with Fine-Grained Quantization
Naveen Mellempudi, Abhisek Kundu, Dheevatsa Mudigere +3
We propose a novel fine-grained quantization (FGQ) method to ternarize pre-trained full precision models, while also constraining activations to 8 and 4-bits. Using this method, we…
Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point
Naveen Mellempudi, Abhisek Kundu, Dipankar Das +2
We propose a cluster-based quantization method to convert pre-trained full precision weights into ternary weights with minimal impact on the accuracy. In addition, we also constrai…