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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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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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04/12/2021
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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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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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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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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06/11/2019
Automatic Model Parallelism for Deep Neural Networks with Compiler and Hardware Support
The deep neural networks (DNNs) have been enormously successful in tasks...
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05/29/2019
Mixed Precision Training With 8-bit Floating Point
Reduced precision computation for deep neural networks is one of the key...
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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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09/04/2018
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers
As deep learning methods form a critical part in commercially important ...
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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
RAIL: Risk-Averse Imitation Learning
Imitation learning algorithms learn viable policies by imitating an expe...
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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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01/31/2017