Pradeep Dubey

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Systolic Computing on GPUs for Productive Performance

We propose a language and compiler to productively build high-performanc...
10/29/2020 ∙ by Hongbo Rong, et al. ∙ 0 ∙ share

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MISIM: An End-to-End Neural Code Similarity System

Code similarity systems are integral to a range of applications from cod...
06/05/2020 ∙ by Fangke Ye, et al. ∙ 6 ∙ share

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Context-Aware Parse Trees

The simplified parse tree (SPT) presented in Aroma, a state-of-the-art c...
03/24/2020 ∙ by Fangke Ye, et al. ∙ 7 ∙ share

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K-TanH: Hardware Efficient Activations For Deep Learning

We propose K-TanH, a novel, highly accurate, hardware efficient approxim...
09/17/2019 ∙ by Abhisek Kundu, et al. ∙ 0 ∙ share

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A Study of BFLOAT16 for Deep Learning Training

This paper presents the first comprehensive empirical study demonstratin...
05/29/2019 ∙ by Dhiraj Kalamkar, et al. ∙ 0 ∙ share

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SysML: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
03/29/2019 ∙ by Alexander Ratner, et al. ∙ 0 ∙ share

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Mixed Precision Training of Convolutional Neural Networks using Integer Operations

The state-of-the-art (SOTA) for mixed precision training is dominated by...
02/03/2018 ∙ by Dipankar Das, et al. ∙ 0 ∙ share

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On Scale-out Deep Learning Training for Cloud and HPC

The exponential growth in use of large deep neural networks has accelera...
01/24/2018 ∙ by Srinivas Sridharan, et al. ∙ 0 ∙ share

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Galactos: Computing the Anisotropic 3-Point Correlation Function for 2 Billion Galaxies

The nature of dark energy and the complete theory of gravity are two cen...
08/31/2017 ∙ by Brian Friesen, et al. ∙ 0 ∙ share

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Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data

This paper presents the first, 15-PetaFLOP Deep Learning system for solv...
08/17/2017 ∙ by Thorsten Kurth, et al. ∙ 0 ∙ share

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Ternary Residual Networks

Sub-8-bit representation of DNNs incur some discernible loss of accuracy...
07/15/2017 ∙ by Abhisek Kundu, et al. ∙ 0 ∙ share

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Ternary Neural Networks with Fine-Grained Quantization

We propose a novel fine-grained quantization (FGQ) method to ternarize p...
05/02/2017 ∙ by Naveen Mellempudi, et al. ∙ 0 ∙ share

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Parallelizing Word2Vec in Multi-Core and Many-Core Architectures

Word2vec is a widely used algorithm for extracting low-dimensional vecto...
11/18/2016 ∙ by Shihao Ji, et al. ∙ 0 ∙ share

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Faster CNNs with Direct Sparse Convolutions and Guided Pruning

Phenomenally successful in practical inference problems, convolutional n...
08/04/2016 ∙ by Jongsoo Park, et al. ∙ 0 ∙ share

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Parallelizing Word2Vec in Shared and Distributed Memory

Word2Vec is a widely used algorithm for extracting low-dimensional vecto...
04/15/2016 ∙ by Shihao Ji, et al. ∙ 0 ∙ share

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BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies

We propose BlackOut, an approximation algorithm to efficiently train mas...
11/21/2015 ∙ by Shihao Ji, et al. ∙ 0 ∙ share

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Pradeep Dubey

Featured Co-authors

Michael I. Jordan

Jian Zhang

Ce Zhang

Dawn Song

Christopher Ré

Yiran Chen

Martin Jaggi

Joseph E. Gonzalez

Jing Li

Andrew Gordon Wilson

Hai Li

Systolic Computing on GPUs for Productive Performance

MISIM: An End-to-End Neural Code Similarity System

Context-Aware Parse Trees

K-TanH: Hardware Efficient Activations For Deep Learning

A Study of BFLOAT16 for Deep Learning Training

SysML: The New Frontier of Machine Learning Systems

Mixed Precision Training of Convolutional Neural Networks using Integer Operations

On Scale-out Deep Learning Training for Cloud and HPC

Galactos: Computing the Anisotropic 3-Point Correlation Function for 2 Billion Galaxies

Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data

Ternary Residual Networks

Ternary Neural Networks with Fine-Grained Quantization

Parallelizing Word2Vec in Multi-Core and Many-Core Architectures

Faster CNNs with Direct Sparse Convolutions and Guided Pruning

Parallelizing Word2Vec in Shared and Distributed Memory

BlackOut: Speeding up Recurrent Neural Network Language Models With Very Large Vocabularies