66 citations · 191 across the 14 of their papers we have counts for
16 papers
Inferring coupling strengths of mixed-mode oscillations in red-giant stars using deep learning
Siddharth Dhanpal, Othman Benomar, Shravan Hanasoge +3
Asteroseismology is a powerful tool that may be applied to shed light on stellar interiors and stellar evolution. Mixed modes, behaving as acoustic waves in the envelope and buoyan…
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…
Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures
Evangelos Georganas, Dhiraj Kalamkar, Kirill Voronin +5
During the past decade, Deep Learning (DL) algorithms, programming systems and hardware have converged with the High Performance Computing (HPC) counterparts. Nevertheless, the pro…
Measuring frequency and period separations in red-giant stars using machine learning
Siddharth Dhanpal, Othman Benomar, Shravan Hanasoge +4
Asteroseismology is used to infer the interior physics of stars. The \textit{Kepler} and TESS space missions have provided a vast data set of red-giant light curves, which may be u…
Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning & HPC Workloads
Evangelos Georganas, Dhiraj Kalamkar, Sasikanth Avancha +16
During the past decade, novel Deep Learning (DL) algorithms, workloads and hardware have been developed to tackle a wide range of problems. Despite the advances in workload and har…
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 Residual Networks
Abhisek Kundu, Kunal Banerjee, Naveen Mellempudi +4
Sub-8-bit representation of DNNs incur some discernible loss of accuracy despite rigorous (re)training at low-precision. Such loss of accuracy essentially makes them equivalent to…
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…
A Randomized Rounding Algorithm for Sparse PCA
Kimon Fountoulakis, Abhisek Kundu, Eugenia-Maria Kontopoulou +1
We present and analyze a simple, two-step algorithm to approximate the optimal solution of the sparse PCA problem. Our approach first solves a L1 penalized version of the NP-hard s…
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…
Recovering PCA from Hybrid- Sparse Sampling of Data Elements
Abhisek Kundu, Petros Drineas, Malik Magdon-Ismail
This paper addresses how well we can recover a data matrix when only given a few of its elements. We present a randomized algorithm that element-wise sparsifies the data, retaining…
Relaxed Leverage Sampling for Low-rank Matrix Completion
Abhisek Kundu
We consider the problem of exact recovery of any matrix of rank from a small number of observed entries via the standard nuclear norm minimization framework.…
A Note on Randomized Element-wise Matrix Sparsification
Abhisek Kundu, Petros Drineas
Given a matrix A \in R^{m x n}, we present a randomized algorithm that sparsifies A by retaining some of its elements by sampling them according to a distribution that depends on b…
Identifying Influential Entries in a Matrix
Abhisek Kundu, Srinivas Nambirajan, Petros Drineas
For any matrix A in R^(m x n) of rank ρ, we present a probability distribution over the entries of A (the element-wise leverage scores of equation (2)) that reveals the most influe…