Towards a high performance neural branch predictor

Vintan, Lucian; Iridon, M.

doi:10.1109/ijcnn.1999.831066

Cited by 34 publications

(19 citation statements)

References 8 publications

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“…Recent work using perceptrons improve predictor accuracy by using longer global histories without exponentially increasing hardware size. The idea of neural branch prediction was originally introduced by Vinton and Iridon [29]. Jiménez et al [9] propose a dynamic Perceptron predictor which exploits long branch histories by increasing hardware table size linearly with the history length.…”

Section: Prior Workmentioning

confidence: 99%

Spotlight - a low complexity highly accurate profile-based branch predictor

Verma

Maderazo

Koppelman

2009

2009 IEEE 28th International Performance Computing and Communications Conference

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In an effort to achieve the high prediction accuracy needed to attain high instruction throughputs, branch predictors proposed in the literature and used in real systems have become increasingly more complex and larger over time. This is not consistent with the anticipated trend of simpler and more numerous cores in future multi-core processors. We introduce the Spotlight Branch predictor, a novel profile-based predictor which is able to achieve high prediction accuracy despite its simple design. Spotlight achieves high accuracy because complex decisions in the prediction process are made during an OS managed, one-time profile run instead of using complex hardware. We show that Spotlight achieves higher accuracy than Gshare as well as highly accurate and implementable predictors such as YAGS and the Hybrid Bimodal-Gshare predictor. It achieves an average reduction in misprediction rate of 20% over Gshare, 11% over Elastic History Buffer, 14% over Yags and 10% over Hybrid for a hardware budget of 8 kB. Spotlight is also compared to two difficult to implement neural predictors, the Path-based Neural and the Hashed Perceptron. It outperforms the Path-based Neural predictor at all sizes and the Hashed Perceptron at smaller hardware budgets. These results demonstrate that a simple profile-based predictor can achieve many of the benefits of more complex predictors. We also show that a single cycle latency implementation of Spotlight can be achieved without sacrificing accuracy by using an upstream replacement scheme.

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Section: Prior Workmentioning

confidence: 99%

Spotlight - a low complexity highly accurate profile-based branch predictor

Verma

Maderazo

Koppelman

2009

2009 IEEE 28th International Performance Computing and Communications Conference

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“…13,14 Perceptron prediction is a two-level scheme using perceptrons instead of two-bit counters as the prediction unit. A perceptron is a simple implementation of a neural network that provides predictive capabilities.…”

Section: Predictors Simulatedmentioning

confidence: 99%

Better Branch Prediction Through Prophet/Critic Hybrids

et al. 2005

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“…We employ a perceptron predictor [33,34]. We also have studied other branch predictors, such as g-share [35].…”

Section: Branch Predictionmentioning

confidence: 99%

Architectural impact of stateful networking applications

Verdú¹,

García²,

Nemirovsky³

et al. 2005

Proceedings of the 2005 ACM Symposium on Architecture for Networking and Communications Systems

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The explosive and robust growth of the Internet owes a lot to the "end-to-end principle", which pushes stateful operations to the end-points. The Internet grew both in traffic volume, and in the richness of the applications it supports. The growth also brought along new security issues and network monitoring applications. Edge devices, in particular, tend to perform upper layer packet processing. A whole new class of applications require stateful processing.In this paper we study the impact of stateful networking applications on architectural bottlenecks. The analysis covers applications with a variety of statefulness levels. The study emphasizes the data cache behavior. Nevertheless, we also discuss other issues, such as branch prediction and ILP. Additionally, we analyze the architectural impact through the TCP connection life. Our results show an important memory bottleneck due to maintaining the states. Moreover, depending on the target of the application, the memory bottleneck may be concentrated within a set of packets or distributed along the TCP connection lifetime.

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Towards a high performance neural branch predictor

Cited by 34 publications

References 8 publications

Spotlight - a low complexity highly accurate profile-based branch predictor

Spotlight - a low complexity highly accurate profile-based branch predictor

Better Branch Prediction Through Prophet/Critic Hybrids

Architectural impact of stateful networking applications

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