Static methods in hybrid branch prediction

Grunwald, D.; Lindsay, D.; Zorn, B.

doi:10.1109/pact.1998.727254

Cited by 15 publications

(5 citation statements)

References 15 publications

(10 reference statements)

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“…The result is not surprising. A lot of work, for example [18], has shown that a significant part of branches are highly biased and many of them are always taken or not-taken during the program execution. Another important reason is a wellknown fact that most program execution time is spent on a small number of program hot paths, which can be easily learnt through training.…”

Section: Discussionmentioning

confidence: 99%

Anomalous path detection with hardware support

Zhang

Zhuang

Pande

et al. 2005

Proceedings of the 2005 International Conference on Compilers, Architectures and Synthesis for Embedded Systems

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Embedded systems are being deployed as a part of critical infrastructures and are vulnerable to malicious attacks due to internet accessibility. Intrusion detection systems have been proposed to protect computer systems from unauthorized penetration. Detecting an attack early on pays off since further damage is avoided and in some cases, resilient recovery could be adopted. This is especially important for embedded systems deployed in critical infrastructures such as Power Grids etc. where a timely intervention could save catastrophes. An intrusion detection system monitors dynamic program behavior against normal program behavior and raises an alert when an anomaly is detected. The normal behavior is learnt by the system through training and profiling.However, all current intrusion detection systems are purely software based and thus suffer from large performance degradation due to constant monitoring operations inserted in application code. Due to the potential performance overheads, software based solutions cannot monitor program behavior at a very fine level of granularity, thus leaving potential security holes as shown in the literature. Another important drawback of such methods is that they are unable to detect intrusions in near real time and the time lag could prove disastrous in real time embedded systems. In this paper, we propose a hardware-based approach to verify program execution paths of target applications dynamically and to detect anomalous executions. With hardware support, our approach offers multiple advantages over software based solutions including minor performance degradation, much stronger detection capability (a larger variety of attacks get detected) and zero-latency reaction upon an anomaly for near real time detection and thus much better security.

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Section: Discussionmentioning

confidence: 99%

Anomalous path detection with hardware support

Zhang

Zhuang

Pande

et al. 2005

Proceedings of the 2005 International Conference on Compilers, Architectures and Synthesis for Embedded Systems

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“…Grunwald et al [8] argue that hybrid predictors are best guided by static information. They classify branches by which component of the hybrid predictor most accurately predicts each branch.…”

Section: Related Workmentioning

confidence: 99%

Branch transition rate: a new metric for improved branch classification analysis

Haungs

Sallee

Farrens

Proceedings Sixth International Symposium on High-Performance Computer Architecture. HPCA-6 (Cat. No.PR00550)

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Recent studies have shown significantly improved branch prediction through the use of branch classification. By separating static branches into groups, or classes, with similar dynamic behavior, predictors may be selected that are best suited for each class. Previous methods have classified branches according to taken rate (or bias). We propose a new metric for branch classification: branch transition rate, which is defined as the number of times a branch changes direction between taken and not taken during execution. We show that transition rate is a more appropriate indicator of branch behavior than taken rate for determining predictor performance. When both metrics are combined, an even clearer picture of dynamic branch behavior emerges, in which expected predictor performance for a branch is closely correlated with its combined taken and transition rate class. Using this classification, a small group of branches is identified for which two-level predictors are ineffective.

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“…This approach achieves better accuracy with respect to a pure dynamic predictor. Finally, in [16], the authors propose to replace the hardware selector of a hybrid predictor with a static information encoded in the branch instruction.…”

Section: Introductionmentioning

confidence: 99%