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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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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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SEERL: Sample Efficient Ensemble Reinforcement Learning
Ensemble learning is a very prevalent method employed in machine learnin...
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K-TanH: Hardware Efficient Activations For Deep Learning
We propose K-TanH, a novel, highly accurate, hardware efficient approxim...
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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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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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Mixed Precision Training With 8-bit Floating Point
Reduced precision computation for deep neural networks is one of the key...
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A Study of BFLOAT16 for Deep Learning Training
This paper presents the first comprehensive empirical study demonstratin...
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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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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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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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RAIL: Risk-Averse Imitation Learning
Imitation learning algorithms learn viable policies by imitating an expe...
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Ternary Residual Networks
Sub-8-bit representation of DNNs incur some discernible loss of accuracy...
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Ternary Neural Networks with Fine-Grained Quantization
We propose a novel fine-grained quantization (FGQ) method to ternarize p...
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Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point
We propose a cluster-based quantization method to convert pre-trained fu...
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