awesome-edge-intelligence-collections
github.com/fangvv/awesome-edge-intelligence-collections ↗About DNN compression and acceleration on Edge Devices.
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PaperNetwork CompressionConditional (Adaptive) ComputingHardware AcceleratorConference PapersPapersArticlesToolsQuantizationPruningKnowledge DistallationNetwork ArchitectureMatrix Factorization(Low-rank Approximation)
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Conference Papers
Papers
- Accelerating Very Deep Convolutional Networks for Classification and DetectionLow Rank Approximation
- AddressNet: Shift-based Primitives for Efficient Convolutional Neural NetworksArchitecture
- AMC: AutoML for model compression and acceleration on mobile devicesPruning
- A Survey of Model Compression and Acceleration for Deep Neural NetworksGeneral
- Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware AccelerationBinarization
- Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1Binarization
Paper
- Accelerating Very Deep Convolutional Networks for Classification and Detection
- A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer LearninKnowledge Distillation
- An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things DevicesSystem
- An Introduction to different Types of Convolutions in Deep LearningReferences
- Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network AccuracyKnowledge Distillation
- Beyond Filters: Compact Feature Map for Portable Deep ModelMiscellaneous
Hardware Accelerator
- DAWNBenchBenchmark and Platform Analysis
- MLPerfBenchmark and Platform Analysis
- Neural-Networks-on-SiliconConvolutional Neural Networks
Articles
Conditional (Adaptive) Computing
Showing a sample of 219 resources. View the full list on GitHub →