Exploring branch target buffer access filtering for low-energy and high-performance microarchitectures

S. Wang, J. Hu, S. G. Ziavras

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Powerful branch predictors along with a large branch target buffer (BTB) are employed in superscalar and simultaneous multi-threading (SMT) processors for instruction-level parallelism and thread-level parallelism exploitation. However, the large BTB not only dominates the predictor energy consumption, but also becomes a major roadblock in achieving faster clock frequencies at deep sub-micron technologies. The authors propose here a filtering scheme to dramatically reduce the accesses to the BTB to achieve significantly reduced energy consumption in the BTB while maintaining the performance. For a simulated superscalar microprocessor, the experimental evaluation shows that the BTB access filtering (BAF) design achieves an 88.5% dynamic energy reduction with negligible performance loss. The authors also study the leakage behaviour and its control in the BAF design. The results show that by applying a drowsy strategy, very effective leakage control can be achieved. For the high-performance design, the BAF can also improve BTB's performance scalability at new technologies. For the simultaneous multi-threading environment, the authors evaluate the effectiveness of the BAF design and propose a banked BAF (BK-BAF) scheme to further reduce the energy consumption and performance overhead. The experimental results confirm that the BK-BAF scheme can be an energy/performance-effective design for next generation SMT processors.

Original languageEnglish (US)
Pages (from-to)50-58
Number of pages9
JournalIET Computers and Digital Techniques
Volume6
Issue number1
DOIs
StatePublished - Jan 2012

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Exploring branch target buffer access filtering for low-energy and high-performance microarchitectures'. Together they form a unique fingerprint.

Cite this