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Automatic Attention Pruning: Improving and Automating Model Pruning using Attentions
Pruning is a promising approach to compress deep learning models in orde...
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Iterative Activation-based Structured Pruning
Deploying complex deep learning models on edge devices is challenging be...
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Adaptive Activation-based Structured Pruning
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Automated Backend-Aware Post-Training Quantization
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UNIT: Unifying Tensorized Instruction Compilation
Because of the increasing demand for computation in DNN, researchers dev...
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Efficient Execution of Quantized Deep Learning Models: A Compiler Approach
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