Orthonormal Data Collaboration (ODC) enforces orthonormal secret and target bases so that alignment reduces to the Orthogonal Procrustes problem, yielding O(acl^2) complexity, orthogonal concordance, and downstream performance invariant to the choice of target basis.
Deng, The mnist database of handwritten digit images for machine learning research [best of the web], IEEE signal processing magazine 29 (6) (2012) 141–142
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Photonic accelerators hit a topology-driven Utilization Wall; symmetric grids improve utilization up to 6X and cut memory access over 40% versus linear layouts.
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Data Collaboration Analysis with Orthonormal Basis Selection and Alignment
Orthonormal Data Collaboration (ODC) enforces orthonormal secret and target bases so that alignment reduces to the Orthogonal Procrustes problem, yielding O(acl^2) complexity, orthogonal concordance, and downstream performance invariant to the choice of target basis.
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Towards Topology-Aware Very Large-Scale Photonic AI Accelerators
Photonic accelerators hit a topology-driven Utilization Wall; symmetric grids improve utilization up to 6X and cut memory access over 40% versus linear layouts.