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11/11/2022
DistGNN-MB: Distributed Large-Scale Graph Neural Network Training on x86 via Minibatch Sampling
Training Graph Neural Networks, on graphs containing billions of vertice...
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04/14/2021
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks
Full-batch training on Graph Neural Networks (GNN) to learn the structur...
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04/12/2021
Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning Workloads
During the past decade, novel Deep Learning (DL) algorithms/workloads an...
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04/12/2021
AI Powered Compiler Techniques for DL Code Optimization
Creating high performance implementations of deep learning primitives on...
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03/19/2021
GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks
Graph Neural Networks (GNNs) use a fully-connected layer to extract feat...
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07/13/2020
Deep Graph Library Optimizations for Intel(R) x86 Architecture
The Deep Graph Library (DGL) was designed as a tool to enable structure ...
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07/02/2020
Hardware Acceleration of Sparse and Irregular Tensor Computations of ML Models: A Survey and Insights
Machine learning (ML) models are widely used in many domains including m...
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06/02/2020
PolyDL: Polyhedral Optimizations for Creation of High Performance DL primitives
Deep Neural Networks (DNNs) have revolutionized many aspects of our live...
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02/06/2020
PolyScientist: Automatic Loop Transformations Combined with Microkernels for Optimization of Deep Learning Primitives
At the heart of deep learning training and inferencing are computational...
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01/15/2020
SEERL: Sample Efficient Ensemble Reinforcement Learning
Ensemble learning is a very prevalent method employed in machine learnin...
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08/29/2019
High Performance Scalable FPGA Accelerator for Deep Neural Networks
Low-precision is the first order knob for achieving higher Artificial In...
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06/15/2019
High-Performance Deep Learning via a Single Building Block
Deep learning (DL) is one of the most prominent branches of machine lear...
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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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08/16/2018
Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures
Convolution layers are prevalent in many classes of deep neural networks...
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08/10/2018
Hierarchical Block Sparse Neural Networks
Sparse deep neural networks(DNNs) are efficient in both memory and compu...
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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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07/20/2017
