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Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities
Relevantly, the non-magnitude-based nature of DPP allows for joint optimization of pruning and weight quantization in order to even further compress the network, which we show as well.
Dynamic Probabilistic Pruning: Training sparse networks based on stochastic and dynamic masking
Deep Learning (DL) models are known to be heavily over-parametrized, resulting in a large memory footprint and power consumption.
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