AI RESEARCH
LegoNet: Memory Footprint Reduction Through Block Weight Clustering
arXiv CS.LG
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ArXi:2603.06606v1 Announce Type: new As the need for neural network-based applications to become accurate and powerful grows, so too does their size and memory footprint. With embedded devices, whose cache and RAM are limited, this growth hinders their ability to leverage state-of-the-art neural network architectures. In this work, we propose \textbf{LegoNet}, a compression technique that \textbf{constructs blocks of weights of the entire model regardless of layer type} and clusters these induced blocks.