66 citations · 191 across the 14 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
Approximating Sparse PCA from Incomplete Data
Abhisek Kundu, Petros Drineas, Malik Magdon-Ismail
We study how well one can recover sparse principal components of a data matrix using a sketch formed from a few of its elements. We show that for a wide class of optimization probl…