Two-level branch prediction using neural networks

Egan, Colin; Steven, G.B.; Quick, Patrick; Anguera, R.; Steven, F.L.; Vintan, Lucian

doi:10.1016/s1383-7621(03)00095-x

Cited by 15 publications

(13 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In comparison with the artificial neural network branch prediction systems [5,4,2] the evolutionary system cannot compete with its accuracy. The neural network prediction systems achieve 75% -90% accuacy.…”

Section: Discussionmentioning

confidence: 99%

Branch predictor on-line evolutionary system

Slany

2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

In this work a branch prediction system which utilizes evolutionary techniques is introduced. It allows the predictor to adapt to the executed code and thus to improve its performance on the fly. Experiments with the predictor system were performed and the results display how various parameters can impact its performance on various executed code. It is evident that a one-level predictor can be evolved whose performance is better than comparable predictors of the same class. The dynamic prediction system predicts with a relative high accuracy and outperforms any static predictor of the same class.

show abstract

Section: Discussionmentioning

confidence: 99%

Branch predictor on-line evolutionary system

Slany

2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…71 Hence, an improvement in BP accuracy may not translate into corresponding performance improvement. Yet, several works only report BP accuracy 1,2,16,19,21,23,25,30,34,43,50,51,55,59,[62][63][64]70,72,75,79,80,82,83,87,91,95,103 without reporting system-performance, and hence, they may provide misleading impression of the potential of a BP management technique. To address this issue, researchers need to adopt comprehensive evaluation methodology where both accuracy and performance impact of BPs are evaluated.…”

Section: Conclusion and Future Outlookmentioning

confidence: 99%

A survey of techniques for dynamic branch prediction

Mittal

2018

Concurrency and Computation

View full text Add to dashboard Cite

Branch predictor (BP) is an essential component in modern processors since high BP accuracy can improve performance and reduce energy by decreasing the number of instructions executed on wrong-path. However, reducing the latency and storage overhead of BP while maintaining high accuracy presents significant challenges. In this paper, we present a survey of dynamic branch prediction techniques. We classify the works based on key features to underscore their differences and similarities. We believe this paper will spark further research in this area and will be useful for computer architects, processor designers, and researchers. KEYWORDSdynamic branch predictor, hybrid BP, neural BP, perceptron predictorside BP, pipelining, predictor accuracy, review, side BP, speculative execution, two-level BP INTRODUCTIONControl-changing instructions such as branches add uncertainty in the execution of dependent instructions and thus lead to large performance loss in pipelined processors. To offset their overhead, accurate prediction of branch outcomes is vital. Since branch misprediction incurs high latency (eg, 14 to 25 cycles 1 ) and wastes energy due to execution of instructions on wrong-path, an improvement in BP* accuracy can boost performance and energy efficiency significantly. For example, experiments on real processors showed that reducing the branch mispredictions by half improved the processor performance by 13%. 2Effective design and management of BPs, however, presents several challenges. Design of BP involves a strict tradeoff between area, energy, accuracy, and latency. An increase in BP complexity for improving accuracy may make it infeasible for implementation or offset its performance benefit. For example, a 512 KB perceptron predictor may provide lower instruction-per-cycle than its 32 KB version, 3 and a 2-cycle fully accurate BP may provide lower instruction-per-cycle than a 1-cycle partially inaccurate BP. 4 Further, due to its high access frequency, BP becomes a thermal hot-spot, 5 and hence, reducing its dynamic and leakage energy is important. Clearly, intelligent techniques are required for resolving these issues.Further, recently, researchers have demonstrated two security vulnerabilities viz., "Meltdown" and "Spectre," which exploit speculative execution feature of modern CPUs. 6-8 For example, Spectre vulnerability works on the observation that the speculative execution due to a branch misprediction may leave side effects, which can reveal sensitive data to attackers, eg, if the memory accesses performed during speculative execution depends on sensitive data, the resulting state of the cache forms a side-channel through which the sensitive data can be leaked. These vulnerabilities affect nearly every modern CPU. While avoiding speculative execution (eg, disabling branch prediction) can mitigate these vulnerabilities, it leads to significant performance loss. In light of these vulnerabilities and associated tradeoffs, a deeper study of branch predictors is definitely required. Contributions:In this ...

show abstract

“…There has been some recent work that adapts machine learning techniques to dynamic hardware prediction. To the best of our knowledge, prior to our original DDT report [33] there was only one such proposal [35] (also see [32] for a more recent presentation), which explored the use of multi-layer neural networks for dynamic branch prediction. Later work explored the use of singlelayer neural networks, known as perceptrons, again for branch prediction.…”

Section: Related Workmentioning

confidence: 99%

“…The multi-layer neural networks in [32] were shown to have promising accuracy in the branch prediction domain. However, there remain serious, unresolved hardware implementation issues.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic feature selection for hardware prediction

Fern

Givan

Falsafi

et al. 2006

Journal of Systems Architecture

View full text Add to dashboard Cite

Most hardware predictors are table based (e.g. two-level branch predictors) and have exponential size growth in the number of input bits or features (e.g. previous branch outcomes). This growth severely limits the amount of predictive information that such predictors can use. To avoid exponential growth we introduce the idea of "dynamic feature selection" for building hardware predictors that can use a large amount of predictive information. Based on this idea, we design the dynamic decision tree (DDT) predictor, which exhibits only linear size growth in the number of features. Our initial evaluation, in branch prediction, shows that the general-purpose DDT, using only branch-history features, is comparable on average to conventional branch predictors, opening the door to practically using large numbers of additional features.

show abstract

Two-level branch prediction using neural networks

Cited by 15 publications

References 23 publications

Branch predictor on-line evolutionary system

Branch predictor on-line evolutionary system

A survey of techniques for dynamic branch prediction

Dynamic feature selection for hardware prediction

Contact Info

Product

Resources

About