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Eliminating redundant data copying can double memory efficiency and dramatically boost accelerator performance in machine learning workloads.
Sparsity unlocks surprisingly practical GPU acceleration for fully homomorphic encrypted neural networks, achieving a 3x speedup and sub-quadratic complexity.
