AI Research
TinyCeNN-LM: Quality-Gated Conversion of Pretrained Attention with CeNN-Inspired Cellular-Recurrent Layers
Bridging the gap between transformer performance and efficient cellular-recurrent architectures.
Replacing attention in a pretrained language model is a compatibility problem: a plausible substitute may alter representations expected by later layers. TinyCeNN-LM introduces a \emph{quality-gated post-training conversion} framework using CeNN-inspired cellular-recurrent layers...
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ArXiv CS.AI
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ArXiv CS.AI
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TinyCeNN-LM demonstrates that we can replace resource-heavy attention mechanisms with efficient cellular-recurrent layers without sacrificing model accuracy. This research provides a crucial pathway for deploying sophisticated language models on edge hardware constrained by strict power and memory limitations.
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