Deep Convolutional Neural Network Architecture
Motivation
massive data movements and computational complexity of DCNNsapp
- performance: CPU
- power consumption: GPU
- fine-grained computing and routing resources still limit the power efficiency and runtime reconfiguration: FPGA
Contributions
- a hybrid data reuse pattern for different layer sizes
- computing resources reconfigurable: a highly scalable and efficient mapping method
- a layer-based scheduling framework: optimization on both power efficiency and performance
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相關文章
- 1. Convolutional Neural Network Architecture
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- 10. 【UFLDL】Exercise: Convolutional Neural Network