科研PPT模板Template1
27页1、ThiNet A Filter Level Pruning Method for Deep Neural Network Compression Contents Background 1 Motivation 2 n Filter Selection n Greedy Method n Minimize Reconstruction Error Proposed Method 3 Experimental Results 4 Conclusion 5 2 Contents Background 1 Motivation 2 n Filter Selection n Greedy Method n Minimize Reconstruction Error Proposed Method 3 Experimental Results 4 Conclusion 5 3 Background Deep Neural Network DNN is hard to deployed on hardware with the limitation of computation resources
2、 storage battery power Figure Performance and model size of different models on ImageNet 4 Model Compression Existing Compression Methods n Quantization convert full precision weights to low precision version e g INQ BWN TWN n Pruning remove less important weights filters from the model e g Deep Compression DNS ThiNet n Design new structure SqueezeNet Distilling ShuffleNet 5 Pruning Methods n Non structured Pruning remove less important weights n Structured Pruning remove less important filters
3、from model 6 Contents Background 1 Motivation 2 n Filter Selection n Greedy Method n Minimize Reconstruction Error Proposed Method 3 Experimental Results 4 Conclusion 5 7 Motivation Problems of Non structured Pruning n Need specialized hardware and software for inference n Ignore cache and memory issues which leads to limited practical acceleration Benefits of Structured Pruning n No change of network structure and can supported by existing deep learning libraries n Reduce the memory and acceler
4、ate inference 8 Contents Background 1 Motivation 2 n Filter Selection n Greedy Method n Minimize Reconstruction Error Proposed Method 3 Experimental Results 4 Conclusion 5 9 Proposed Method ThiNet Thin Net a filter level pruning compression framework for model compression Figure Illustration of ThiNet Filter Selection Pruning Fine tuning 10 Proposed Method Framework of ThiNet 11 Filter Selection n Convolution operation can be computed as follows 1 12 Filter Selection n Define n Then 2 3 13 Greed
5、y Method 4 5 14 Greedy Method n Use greedy method to solve the optimization problem A greedy algorithm for minimizing Eq 5 15 Minimize the Reconstruction Error n Minimize the reconstruction error by weighting the channels n Eq 6 can be solved by the ordinary least squares approach 6 7 16 Contents Background 1 Motivation 2 n Filter Selection n Greedy Method n Minimize Reconstruction Error Proposed Method 3 Experimental Results 4 Conclusion 5 17 Pruning Strategy n VGG 16 prune the first 10 convolu
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