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Adaptive Information Processing: An Effective Way to Improve Perceptron Predictors. Hongliang Gao Huiyang Zhou. Correlation feedback. Information system model. Information system model by Chen et. al. [ASPLOS-VII]. Key observations Shortcomings:

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adaptive information processing an effective way to improve perceptron predictors

Adaptive Information Processing: An Effective Way to Improve Perceptron Predictors

Hongliang Gao Huiyang Zhou

information system model

Correlation feedback

Information system model
  • Information system model by Chen et. al. [ASPLOS-VII].
  • Key observations
    • Shortcomings:
      • Fixed information vector while different workloads/branches need different information data.
    • Perceptron weights  Correlation
      • Assemble information vector to maximize correlation

Predictor

FSMs

Perceptrons [2]

Source

(program)

Information

processor

Information:

Br address

Br history

Br type

Other run time

info.

Information

vector

  • Our contribution
    • Re-assemble the information vector based on correlation (weights)
    • Performed at a coarse grain, so it is not latency critical

University of Central Florida

adaptive information processing

Inputs: m bits

Controls

MUX

MUX

Output: n bits

n < m

Adaptive Information Processing

Information

  • Profile-directed adaptation
  • Correlation-directed adaptation

LHR

GHR

PC

Fixed

Perceptron Predictor

University of Central Florida

profile directed adaptation

4

4

4

4

type

type

type

… …

MUX1

4

4

4

Workload

Detector

type

MAC-RHSP predictor [5]

Index N

Index 1

Index 0

Index 2

Profile-directed adaptation

Information

LHR table

GHR

PC

Information vector

4

wt table N

wt table 1

wt table 2

w0 table

… …

weights

weights

weights

Update

Logic

Prediction = sign(y)

y

University of Central Florida

profile directed adaptation1

L[0:3] L[4:7] G[0:3^4:7] G[8:11^12:15] G[16:19^20:23] G[24:27^28:31] G[32:35^36:39] …… G[80:83^84:87]

P[0:3] L[0:3] G[0:3] G[4:7] G[8:11] G[12:P15] G[16:19] …… G[56:59^60:63]

P[0:3] L[0:3] G[0:3] G[4:7] G[8:11] G[12:P15] G[16:19] …… L[4:7]

P[0:3] P[4:7] P[8:11] P[12:15] P[16:19] G[0:3] G[4:7] …… L[0:3]

FP

INT

MM

L[4:7] L[0:3] L[0:3] P[4:7]

G[ ^ ] G[^] L[4:7] L[0:3]

L[0:3] P[0:3] P[0:3] P[0:3]

4

4

4

4

4

4

4

4

4

4

4

4

SERV

type

type

type

MUX1

MUX1

MUX1

4

4

4

Profile-directed adaptation

FP

G[0:3] XOR G[4:7]

INT

MM

SERV

… …

Table 1

Table 2

Table N

Workload

Detector

type

University of Central Florida

workload detector
Workload Detector
  • Detection criteria
    • SERV: a large number of static branches
    • FP: a small number of static branches,

a high number of floating point operation,

and a high number of instructions using XMM registers

    • MM: a medium number of static branches,

a medium number of floating point operation,

and a medium number of instructions using XMM registers

    • INT: default

Source

(program)

Workload

Detector

type

Run time info:

# of fp insts

XMM accesses

# of static br

University of Central Florida

correlation directed adaptation

Information feeding logic

4

4

4

cN

c2

c1

MUX2

type

MUX1

4

4

cN

c1

c2

Adaptation

Logic

(c1, c2, c3, …)

MAC-RHSP predictor

4

4

4

4

Index N

Index 1

Index 2

Index 0

Correlation-directed adaptation

Information

LHR table

type

type

… …

GHR

PC

Information vector

4

4

wt table N

wt table 1

wt table 2

w0 table

… …

weights

weights

weights

Workload

Detector

Update

Logic

type

Prediction = sign(y)

y

University of Central Florida

correlation directed adaptation1

cN’PC’

c2

c1

GHR

PC

cN

c1

c2

Adaptation

Logic

4

4

4

(c1, c2, c3, …)

Index 1

Index 2

Index N

Correlation-directed adaptation

Information

Information feeding logic

LHR table

4

PC

type

type

type

(INT)

… …

GHR

MUX1

4

4

4

4

PC

MUX2

Information vector

4

4

4

Table 1

Table 2

Table N

PC

LHR

… …

weights

weights

weights

Sum1

Sum2

SumN

Workload

Detector

Large Sum1 => strong correlation with PC bits

Small SumN => weak correlation with certain GHR bits

cN  ‘PC’ => more PC bits

type

University of Central Florida

overall scheme

10

00

01

11

Overall scheme

initial (predict NT)

confidence

LRU

tag

loop count

taken count

always Taken

(predict T)

always NT

(predict NT)

PC

PC

Loop branch predictor

Not biased (use other predictors)

loop hit

loop prediction

Bias branch predictor

GHR

NT

Taken

MAC perceptron

predictor

PC

LHR table

prediction

loop prediction

prediction

runtime

info.

workload detector

loop hit

University of Central Florida

summary
Summary
  • Observations
    • Different workloads/branches need different information.
    • Perceptron weights  Correlation
  • Contributions
    • Profile-directed adaptation
    • Correlation-directed adaptation
    • Reducing aliasing from bias and loop branches
  • Result
    • Significant improvement

University of Central Florida

references
References

[1] I. K. Chen, J. T. Coffey, and T. N. Mudge, “Analysis of branch prediction via data compression”, Proc. of the 7th Int. Conf. on Arch. Support for Programming Languages and Operating Systems (ASPLOS-VII), 1996.

[2] D. Jimenez and C. Lin, “Dynamic branch prediction with perceptrons”, Proc. of the 7th Int. Symp. on High Perf. Comp. Arch (HPCA-7), 2001.

[3] D. Jimenez and C. Lin, “Neural methods for dynamic branch prediction”, ACM Trans. on Computer Systems, 2002.

[4] S. MacFarling, “Combining branch predictors”, Technical Report, DEC, 1993.

[5] A. Seznec, “Revisiting the perceptron predictor”, Technical Report, IRISA, 2004.

[6] T.-Y. Yeh and Y. Patt, “Alternative implementations of two-level adaptive branch prediction”, Proc. of the 22nd Int. Symp. on Comp. Arch (ISCA-22), 1995.

University of Central Florida

predictor configuration
Predictor configuration

University of Central Florida

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