66 citations · 299 across the 20 of their papers we have counts for
7 papers · 1 filter
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…