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.
Crow: a low-cost substrate for improving dram performance, energy efficiency, and reliability
5 Pith papers cite this work. Polarity classification is still indexing.
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IP-CaT jointly optimizes TLB and cache management for L1I prefetching via a translation prefetch buffer and trimodal replacement policy, yielding 8.7% geomean speedup over EPI across 105 server workloads.
HammerSim is a gem5-based full-system framework for modeling RowHammer with probability-driven bitflip simulation, validated against real DDR4 DIMMs via JS divergence.
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.
Eidola is a gem5 extension that emulates cycle-level peer-to-peer GPU writes via real-application timing profiles to simulate traffic and synchronization in multi-GPU AI systems.
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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Enhancing Instruction Prefetching via Cache and TLB Management
IP-CaT jointly optimizes TLB and cache management for L1I prefetching via a translation prefetch buffer and trimodal replacement policy, yielding 8.7% geomean speedup over EPI across 105 server workloads.
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HammerSim: A System-Level Tool to Model RowHammer
HammerSim is a gem5-based full-system framework for modeling RowHammer with probability-driven bitflip simulation, validated against real DDR4 DIMMs via JS divergence.
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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.
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Eidola: Modeling Multi-GPU Network Communication Traffic in Distributed AI Workloads
Eidola is a gem5 extension that emulates cycle-level peer-to-peer GPU writes via real-application timing profiles to simulate traffic and synchronization in multi-GPU AI systems.