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FlowTT: Exploiting Computation Flow Reuse in Irregular Tensor-Train Embedding
Tensor-Train (TT) decomposition effectively compresses large embedding tables in recommendation models, but TT-based embedding lookup remains inefficient because partially shared computation flows across input indices are not fully reused and intermediate results are repeatedly materialized off-chip
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