AIBullisharXiv – CS AI · Jun 237/10
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Breaking chains with trees: Deep learning with $\mathcal{O}(\log N)$ parallel time complexity
Researchers propose Hierarchical Block-Local Learning (HBLL), a novel deep learning framework that trains neural networks with O(log N) parallel time complexity by decomposing networks into hierarchically linked blocks with local learning objectives. This approach eliminates sequential backpropagation constraints, addressing the locking problem and weight transport challenge while maintaining competitive performance on vision and language tasks.