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04/25/2023
Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures
During the past decade, Deep Learning (DL) algorithms, programming syste...
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04/16/2021
Efficient and Generic 1D Dilated Convolution Layer for Deep Learning
Convolutional neural networks (CNNs) have found many applications in tas...
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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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05/10/2020
Optimizing Deep Learning Recommender Systems' Training On CPU Cluster Architectures
During the last two years, the goal of many researchers has been to sque...
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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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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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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
