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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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09/12/2022
FP8 Formats for Deep Learning
FP8 is a natural progression for accelerating deep learning training inf...
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04/22/2022
FPGA-based AI Smart NICs for Scalable Distributed AI Training Systems
Rapid advances in artificial intelligence (AI) technology have led to si...
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02/21/2022
Next-Generation Local Time Stepping for the ADER-DG Finite Element Method
High-frequency ground motion simulations pose a grand challenge in compu...
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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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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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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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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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11/05/2019
Training Neural Machine Translation (NMT) Models using Tensor Train Decomposition on TensorFlow (T3F)
We implement a Tensor Train layer in the TensorFlow Neural Machine Trans...
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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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04/12/2019
Leveraging the bfloat16 Artificial Intelligence Datatype For Higher-Precision Computations
In recent years fused-multiply-add (FMA) units with lower-precision mult...
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10/12/2018
ISA Mapper: A Compute and Hardware Agnostic Deep Learning Compiler
Domain specific accelerators present new challenges and opportunities fo...
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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
