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Optimizing Grouped Convolutions on Edge Devices
When deploying a deep neural network on constrained hardware, it is poss...
06/17/2020 ∙ by Perry Gibson, et al. ∙
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Neural Architecture Search without Training
The time and effort involved in hand-designing deep neural networks is i...
06/08/2020 ∙ by Joseph Mellor, et al. ∙
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Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs
Convolutional Neural Networks (CNN) are becoming a common presence in ma...
02/20/2020 ∙ by Valentin Radu, et al. ∙
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Deep Kernel Transfer in Gaussian Processes for Few-shot Learning
Humans tackle new problems by making inferences that go far beyond the i...
10/11/2019 ∙ by Massimiliano Patacchiola, et al. ∙
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BlockSwap: Fisher-guided Block Substitution for Network Compression
The desire to run neural networks on low-capacity edge devices has led t...
06/10/2019 ∙ by Jack Turner, et al. ∙
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HAKD: Hardware Aware Knowledge Distillation
Despite recent developments, deploying deep neural networks on resource ...
10/24/2018 ∙ by Jack Turner, et al. ∙
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Distilling with Performance Enhanced Students
The task of accelerating large neural networks on general purpose hardwa...
10/24/2018 ∙ by Jack Turner, et al. ∙
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Pruning neural networks: is it time to nip it in the bud?
Pruning is a popular technique for compressing a neural network: a large...
10/10/2018 ∙ by Elliot J. Crowley, et al. ∙
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Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are extremely computationally deman...
09/19/2018 ∙ by Jack Turner, et al. ∙
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