Proves constant-dimension 3 reachability embeddings for directed trees, O(t log n) for treewidth-t graphs, with lower bounds of Omega(n) for general DAGs and Omega(t/log(n/t)) for treewidth t, plus real-dataset results.
Drex: Accurate and scalable dense retrieval acceleration via algorithmic-hardware codesign
4 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
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2026 4representative citing papers
NasZip delivers up to 8.4x speedup over CPU baselines and 1.69x over prior NDP accelerators for ANNS by combining near-data processing with statistics-based PCA early exiting, dynamic-float encoding, and data-aware neighbor mapping.
TTP is a hardware prefetcher for ray tracing that leverages traversal stack addresses during DFS to prefetch BVH nodes, achieving 1.48x average speedup and 98.92% L1 accuracy in cycle-level simulations.
Reference-augmented offline policy optimization through a differentiable RNN dynamics model cuts TDCR tip-position error by ~51% versus non-augmented training and outperforms Jacobian controllers across speeds.
citing papers explorer
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Compact Geometric Representations of Hierarchies
Proves constant-dimension 3 reachability embeddings for directed trees, O(t log n) for treewidth-t graphs, with lower bounds of Omega(n) for general DAGs and Omega(t/log(n/t)) for treewidth t, plus real-dataset results.
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NasZip: Software and Hardware Co-Design to Accelerate Approximate Nearest Neighbor Search with DIMM-Based Near-Data Processing
NasZip delivers up to 8.4x speedup over CPU baselines and 1.69x over prior NDP accelerators for ANNS by combining near-data processing with statistics-based PCA early exiting, dynamic-float encoding, and data-aware neighbor mapping.
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TTP: A Hardware-Efficient Design for Precise Prefetching in Ray Tracing
TTP is a hardware prefetcher for ray tracing that leverages traversal stack addresses during DFS to prefetch BVH nodes, achieving 1.48x average speedup and 98.92% L1 accuracy in cycle-level simulations.
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Reference-Augmented Learning for Precise Tracking Policy of Tendon-Driven Continuum Robots
Reference-augmented offline policy optimization through a differentiable RNN dynamics model cuts TDCR tip-position error by ~51% versus non-augmented training and outperforms Jacobian controllers across speeds.