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To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference
We experimentally show that how two mainstream compression techniques, data quantization and pruning, perform on these network architectures and the implications of compression techniques to the model storage size, inference time, energy consumption and performance metrics.
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