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06/16/2022
Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization
We report on aggressive quantization strategies that greatly accelerate ...
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08/27/2021
4-bit Quantization of LSTM-based Speech Recognition Models
We investigate the impact of aggressive low-precision representations of...
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04/21/2021
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
Large-scale distributed training of Deep Neural Networks (DNNs) on state...
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12/24/2020
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training
Recent breakthroughs in deep neural networks (DNNs) have fueled a tremen...
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01/19/2019
Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks
Efforts to reduce the numerical precision of computations in deep learni...
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12/19/2018
Training Deep Neural Networks with 8-bit Floating Point Numbers
The state-of-the-art hardware platforms for training Deep Neural Network...
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07/17/2018
Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN)
Deep learning algorithms achieve high classification accuracy at the exp...
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05/16/2018
PACT: Parameterized Clipping Activation for Quantized Neural Networks
Deep learning algorithms achieve high classification accuracy at the exp...
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12/07/2017
AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training
Highly distributed training of Deep Neural Networks (DNNs) on future com...
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02/09/2015