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04/16/2021
Accelerating Sparse Deep Neural Networks
As neural network model sizes have dramatically increased, so has the in...
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07/03/2020
Self-Supervised GAN Compression
Deep learning's success has led to larger and larger models to handle mo...
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03/06/2019
Buddy Compression: Enabling Larger Memory for Deep Learning and HPC Workloads on GPUs
GPUs offer orders-of-magnitude higher memory bandwidth than traditional ...
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06/01/2018
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training
Exploiting sparsity enables hardware systems to run neural networks fast...
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04/26/2018
Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip
Recurrent Neural Networks (RNNs) are powerful tools for solving sequence...
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02/18/2018
Efficient Sparse-Winograd Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are computationally intensive, whic...
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05/24/2017
Exploring the Regularity of Sparse Structure in Convolutional Neural Networks
Sparsity helps reduce the computational complexity of deep neural networ...
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05/03/2017
Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks
Popular deep learning frameworks require users to fine-tune their memory...
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07/15/2016
DSD: Dense-Sparse-Dense Training for Deep Neural Networks
Modern deep neural networks have a large number of parameters, making th...
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06/08/2015