Aditya Kusupati


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SHARCS: Efficient Transformers Through Routing with Dynamic Width Sub-networks
Mohammadreza Salehi | Sachin Mehta | Aditya Kusupati | Ali Farhadi | Hannaneh Hajishirzi
Findings of the Association for Computational Linguistics: EMNLP 2023

We introduce SHARCS for adaptive inference that takes into account the hardness of input samples. SHARCS can train a router on any transformer network, enabling the model to direct different samples to sub-networks with varying widths. Our experiments demonstrate that: (1) SHARCS outperforms or complements existing per-sample adaptive inference methods across various classification tasks in terms of accuracy vs. FLOPs; (2) SHARCS generalizes across different architectures and can be even applied to compressed and efficient transformer encoders to further improve their efficiency; (3) SHARCS can provide a 2 times inference speed up at an insignificant drop in accuracy.