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We introduce hybrid pruning which combines both coarse-grained channel andfine-grained weight pruning to reduce model size, computation and power demandswith no to little loss in accuracy for enabling modern networks deployment onresource-constrained devices, such as always-on security cameras and drones.Additionally, to effectively perform channel pruning, we propose a fastsensitivity test that helps us quickly identify the sensitivity of within andacross layers of a network to the output accuracy for target multiplieraccumulators (MACs) or accuracy tolerance. Our experiment shows significantlybetter results on ResNet50 on ImageNet compared to existing work, even with anadditional constraint of channels be hardware-friendly number.

Hybrid Pruning: Thinner Sparse Networks for Fast Inference on Edge Devices