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Max-Affine Spline Insights Into Deep Network Pruning
In this paper, we study the importance of pruning in Deep Networks (DNs)...
01/07/2021 ∙ by Randall Balestriero, et al. ∙
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SmartDeal: Re-Modeling Deep Network Weights for Efficient Inference and Training
The record-breaking performance of deep neural networks (DNNs) comes wit...
01/04/2021 ∙ by Xiaohan Chen, et al. ∙
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FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training
Recent breakthroughs in deep neural networks (DNNs) have fueled a tremen...
12/24/2020 ∙ by Yonggan Fu, et al. ∙
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DNA: Differentiable Network-Accelerator Co-Search
Powerful yet complex deep neural networks (DNNs) have fueled a booming d...
10/28/2020 ∙ by Yongan Zhang, et al. ∙
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ShiftAddNet: A Hardware-Inspired Deep Network
Multiplication (e.g., convolution) is arguably a cornerstone of modern d...
10/24/2020 ∙ by Haoran You, et al. ∙
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SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation
We present SmartExchange, an algorithm-hardware co-design framework to t...
05/07/2020 ∙ by fcq, et al. ∙
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Drawing early-bird tickets: Towards more efficient training of deep networks
(Frankle & Carbin, 2019) shows that there exist winning tickets (small b...
09/26/2019 ∙ by Haoran You, et al. ∙
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Bayesian CycleGAN via Marginalizing Latent Sampling
Recent techniques built on Generative Adversarial Networks (GANs) like C...
11/19/2018 ∙ by Haoran You, et al. ∙
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