PrISM uses a Sampled History Queue to correlate row samples across windows, solving the non-selection problem in probabilistic RowHammer mitigation and cutting slowdown from 10.7% to 1.5% at threshold 250 versus prior methods.
Constant-rate entanglement distillation for fast quantum inter- connects
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Lattice-surgery scheduling is mapped to 3D path embedding and solved with look-ahead Dijkstra projection, yielding 3.8x lower execution time on quantum phase estimation benchmarks versus greedy scheduling.
WaveTune introduces a wave-aware bilinear latency predictor and wave-structured sparse sampling to enable fast runtime auto-tuning of GPU kernels, achieving up to 1.83x kernel speedup and 1.33x TTFT reduction with drastically lower overhead.
citing papers explorer
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Loaded Dice: Solving the Non-Selection Problem for Scalable Probabilistic RowHammer Defense
PrISM uses a Sampled History Queue to correlate row samples across windows, solving the non-selection problem in probabilistic RowHammer mitigation and cutting slowdown from 10.7% to 1.5% at threshold 250 versus prior methods.
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Efficient and high-performance routing of lattice-surgery paths on three-dimensional lattice
Lattice-surgery scheduling is mapped to 3D path embedding and solved with look-ahead Dijkstra projection, yielding 3.8x lower execution time on quantum phase estimation benchmarks versus greedy scheduling.
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WaveTune: Wave-aware Bilinear Modeling for Efficient GPU Kernel Auto-tuning
WaveTune introduces a wave-aware bilinear latency predictor and wave-structured sparse sampling to enable fast runtime auto-tuning of GPU kernels, achieving up to 1.83x kernel speedup and 1.33x TTFT reduction with drastically lower overhead.