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Gilles Pokam

Publications and source records attributed to Gilles Pokam.

2 recordsLinked to original sources

CHiRP: Control-Flow History Reuse Prediction

Translation Lookaside Buffers (TLBs) play a critical role in hardware-supported memory virtualization. To speed up address translation and reduce costly page table walks, TLBs cache a small number of recently-used virtual-to-physical address translations. TLBs must make the best use of their limited capacities. Thus, TLB entries with low potential for reuse should be replaced by more useful entries. This paper contributes to an aspect of TLB management that has received little attention in the literature: replacement policy. We show how predictive replacement policies can be tailored toward TLBs to reduce miss rates and improve overall performance. We begin by applying recently proposed predictive cache replacement policies to the TLB. We show these policies do not work well without considering specific TLB behavior. Next, we introduce a novel TLB-focused predictive policy, Control-flow History Reuse Prediction (CHiRP). This policy uses a history signature and replacement algorithm that correlates to known TLB behavior, outperforming other policies. For a 1024-entry 8-way set-associative L2 TLB with a 4KB page size, we show that CHiRP reduces misses per 1000 instructions (MPKI) by an average 28.21% over the least-recently-used (LRU) policy, outperforming Static Re-reference Interval Prediction (SRRIP), Global History Reuse Policy (GHRP) and SHiP, which reduce MPKI by an average of 10.36%, 9.03% and 0.88%, respectively.

cs.AR↗

PerSpectron: Detecting Invariant Footprints of Microarchitectural Attacks with Perceptron

Detecting microarchitectural attacks is critical given their proliferation in recent years. Many of these attacks exhibit intrinsic behaviors essential to the nature of their operation, such as creating contention or misspeculation. This study systematically investigates the microarchitectural footprints of hardware-based attacks and shows how they can be detected and classified using an efficient hardware predictor. We present a methodology to use correlated microarchitectural statistics to design a hardware-based neural predictor capable of detecting and classifying microarchitectural attacks before data is leaked. Once a potential attack is detected, it can be proactively mitigated by triggering appropriate countermeasures. Our hardware-based detector, PerSpectron, uses perceptron learning to identify and classify attacks. Perceptron-based prediction has been successfully used in branch prediction and other hardware-based applications. PerSpectron has minimal performance overhead. The statistics being monitored have similar overhead to already existing performance monitoring counters. Additionally, PerSpectron operates outside the processor's critical paths, offering security without added computation delay. Our system achieves a usable detection rate for detecting attacks such as SpectreV1, SpectreV2, SpectreRSB, Meltdown, breakingKSLR, Flush+Flush, Flush+Reload, Prime+Probe as well as cache-attack calibration programs. We also believe that the large number of diverse microarchitectural features offers both evasion resilience and interpretability---features not present in previous hardware security detectors. We detect these attacks early enough to avoid any data leakage, unlike previous work that triggers countermeasures only after data has been exposed.

cs.CR↗