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04/14/2023
AUTOSPARSE: Towards Automated Sparse Training of Deep Neural Networks
Sparse training is emerging as a promising avenue for reducing the compu...
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09/12/2022
FP8 Formats for Deep Learning
FP8 is a natural progression for accelerating deep learning training inf...
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10/29/2020
Systolic Computing on GPUs for Productive Performance
We propose a language and compiler to productively build high-performanc...
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06/05/2020
MISIM: An End-to-End Neural Code Similarity System
Code similarity systems are integral to a range of applications from cod...
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03/24/2020
Context-Aware Parse Trees
The simplified parse tree (SPT) presented in Aroma, a state-of-the-art c...
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09/17/2019
K-TanH: Hardware Efficient Activations For Deep Learning
We propose K-TanH, a novel, highly accurate, hardware efficient approxim...
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05/29/2019
A Study of BFLOAT16 for Deep Learning Training
This paper presents the first comprehensive empirical study demonstratin...
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03/29/2019
SysML: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoptio...
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02/03/2018
Mixed Precision Training of Convolutional Neural Networks using Integer Operations
The state-of-the-art (SOTA) for mixed precision training is dominated by...
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01/24/2018
On Scale-out Deep Learning Training for Cloud and HPC
The exponential growth in use of large deep neural networks has accelera...
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08/31/2017
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...
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08/17/2017
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data
This paper presents the first, 15-PetaFLOP Deep Learning system for solv...
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07/15/2017
Ternary Residual Networks
Sub-8-bit representation of DNNs incur some discernible loss of accuracy...
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05/02/2017
Ternary Neural Networks with Fine-Grained Quantization
We propose a novel fine-grained quantization (FGQ) method to ternarize p...
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11/18/2016
Parallelizing Word2Vec in Multi-Core and Many-Core Architectures
Word2vec is a widely used algorithm for extracting low-dimensional vecto...
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08/04/2016
Faster CNNs with Direct Sparse Convolutions and Guided Pruning
Phenomenally successful in practical inference problems, convolutional n...
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04/15/2016
Parallelizing Word2Vec in Shared and Distributed Memory
Word2Vec is a widely used algorithm for extracting low-dimensional vecto...
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11/21/2015