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S4: a High-sparsity, High-performance AI Accelerator
And the degree of sparsity one can exploit has become higher as larger model sizes have been considered along with the trend of pre-training giant models.
Rethinking Network Pruning -- under the Pre-train and Fine-tune Paradigm
In particular, common wisdom in pruning CNN states that sparse pruning technique compresses a model more than that obtained by reducing number of channels and layers (Elsen et al., 2020; Zhu and Gupta, 2017), while existing works on sparse pruning of BERT yields inferior results than its small-dense counterparts such as TinyBERT (Jiao et al., 2020).
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