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An Ahead Pipelined Alloyed Perceptron with Single Cycle Access Time. David Tarjan (CS) Kevin Skadron (CS) Mircea R. Stan (ECE) University of Virginia. The whole presentation in one slide. Perceptron: Best predictor yet, basic unit is table plus adder

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an ahead pipelined alloyed perceptron with single cycle access time

An Ahead Pipelined Alloyed Perceptron with Single Cycle Access Time

David Tarjan (CS)

Kevin Skadron (CS)

Mircea R. Stan (ECE)

University of Virginia

the whole presentation in one slide
The whole presentation in one slide
  • Perceptron: Best predictor yet,

basic unit is table plus adder

  • Ahead pipeline it and precompute, so it has eff. latency 1 and shorter pipeline
  • Accuracy does not suffer:
talk outline
Talk Outline
  • Motivation & Perceptron Theory
  • Related Work
  • Ahead Pipelining a Perceptron
  • Precomputing a local hist. perceptron
  • Results
  • Conclusion
motivation
Motivation

Main Problems in Branch Prediction:

  • Accuracy (larger tables, more logic)
  • Latency (smaller tables, less logic)
  • Multiple Branch/Trace/Stream/etc. per cycle

We address these points

Tradeoff!

motivation1
Motivation
  • Two recent innovations (both by Jiménez):
  • Perceptrons: simplest neural network, better accuracy than any previously known predictor(MICRO’01)
  • Ahead pipelining: Start prediction early, hide latency of prediction, allows access to large tables(HPCA’03)
  • Use of alloyed history: reduces wrong history branch mispredictions(PACT’00)
related work
Related Work
  • Perceptron Branch Predictor: lots of work by Jiménez(HPCA’01, MICRO’03)
  • Perceptron continued: new work by Seznec(TR 1554, 1620) and Ipek (Cornell)
  • Ahead: Gshare.fast(Jiménez, HPCA’03), EV8 Bpred Adaption(Seznec, ISCA’03)
  • Precomputation: Original 2-level Branch Predictor Paper(Yeh & Patt, Micro’91)
conclusion
Conclusion
  • Ahead Pipelining solves latency problem
  • Precomputation allows use of local history in a fast predictor
  • Alloyed Perceptron may offer better accuracy