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Explicit Modeling of Control and Data for Improved NoC Router Estimation. Andrew B. Kahng +* , Bill Lin * and Siddhartha Nath + UCSD CSE + and ECE * Departments { abk , billlin , sinath }@ eng.ucsd.edu. Outline. Motivation Our work: Overview Methodology

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explicit modeling of control and data for improved noc router estimation

Explicit Modeling of Control and Data for Improved NoC Router Estimation

Andrew B. Kahng+*, Bill Lin*

and Siddhartha Nath+

UCSD CSE+ and ECE* Departments

{abk, billlin, sinath}@eng.ucsd.edu

outline
Outline
  • Motivation
  • Our work: Overview
  • Methodology
  • Flit-level power estimation
  • Summary
noc modeling so far orion
NoC Modeling So Far… (ORION)

Arbiter

SRC

BUF I

SINK

Link

BUFE

Link

XBAR

Link

BUFW

Link

Link

BUFN

Link

Link

BUFS

Link

Leakage power

ORION1.0 (2002)

ORION2.0 (2009)

Clock power

6NOR + 2INV + DFF

6NOR + 2INV + DFF

what is the problem
What Is The Problem?

Arbiter

SRC

BUF I

SINK

Link

BUFE

Link

XBAR

Link

BUFW

Link

Link

BUFN

Link

Link

BUFS

Link

6NOR + 2INV + DFF

RTL code mismatch

Logic transformation and technology mapping mismatch

how bad is it
How Bad Is It?

Router RTL generators:

Netmaker – Cambridge, UK

Stanford NoC - Stanford

460%

89%

  • Why such large errors?
    • Assumed logic template inaccurate
    • Control logic not modeled
    • Implementation details missing
outline1
Outline
  • Motivation
  • Our work: Overview
  • Methodology
  • Flit-level power estimation
  • Summary
we propose step 1
We Propose: Step 1
  • Derive router component block parametric models from post-synthesis netlists

~P2

~F

~P2

XBAR ~ P2F

P - #Ports

V - #VCs

B - #BUFs

F – Flit-width

  • Key idea: No assumed logic template
  • Component models derived from actual RTL synthesized with cell libraries
we propose step 2
We Propose: Step 2

XBAR ~ P2F

XBARarea = a1.P2F + a0

LSQR

  • Key idea: Capture implementation details using automatic regression fit
  • Characterization performed only once and usable for multiple design space explorations

Automatic fitting of models with post-P&R power and area

outline2
Outline
  • Motivation
  • Our work: Overview
  • Methodology
  • Flit-level power estimation
  • Summary
model development
Model Development

NoC router RTL generators

µArch params: P, V, B, F

Implparams: Clock Frequency

  • Two RTL generators:
    • Netmaker (Cambridge, UK)
    • Stanford NoC
  • SP&R tools:
    • Cadence RC & Synopsys DC for hierarchical synthesis to analyze each block
    • Cadence SOC Encounter for P&R

Synthesis and P&R: DC/RC, SOCE

Analysis of blocks: XBAR, SW & VC arbiter, Input & Output buffers

New models for each component block

overall methodology
Overall Methodology

ORION_NEW models

Technology

Library

Post P&R

data per block

Basic

Regression fit

Std. cell count

& area

Cell area

Cell leakage

Leakage power

Manual

LSQR

Pin cap.

Internal power

Internal

energy

Switching power

Estimates for gate count

Area

Power: leakage, internal, switching

  • LSQR
    • Accurate (captures implementation details)
    • One-time overhead (generation of P&R training data points)
  • Manual
    • Quick and easy
    • Misses implementation details
results area and power
Results: Area And Power

AREA

POWER

4xreduction

6.5xreduction

Methodology scales across technologies, router RTL generators

outline3
Outline
  • Motivation
  • Our work: Overview
  • Methodology
  • Flit-level power estimation
  • Summary
flit level power estimation
Flit-level Power Estimation

Post-P&R router netlist

Power analysis

Gate-level simulation

Testbench

VCD

ORION_NEW models

Power Report

Regression fit

Flit-level power model

GARNET

gem5

Flit-level power estimates

Dynamic power estimation using flit-level bit encodings

Have integrated with full-system NoC simulator (GARNET)

results flit level power
Results: Flit-level Power

3.6xreduction

  • Accurate estimation of flit-level dynamic power
outline4
Outline
  • Motivation
  • Our work: Overview
  • Methodology
  • Flit-level power estimation
  • Summary
summary
Summary
  • New hybrid modeling methodology: relax the template mindset
    • Explicitly models control and data signals
    • Captures RTL and implementation details
  • Using proposed parametric regression methodology, worst-case estimation errors reduced by a factor of
    • 6.5x from ORION2.0 for power
    • 4x from ORION2.0 for area
  • We propose an application of our methodology for flit-level dynamic power modeling and integration with GARNET
    • 3.6x worst-case error reduction in dynamic power estimation
  • Ongoing: Non-parametric modeling of post-P&R power and area
regression analysis approach
Regression analysis approach

a1. Instsmodel <component> + a0 = Inststool <component>

InstsRmodel<component> = a1. Instsmodel <component> + a0

  • Step 2a: Fit area of each router component with post-layout area

b1. InstsRmodel <component> + b0 = Areatool <component>

  • Step 2b: Fit power of each router component with post-layout power (leakage, internal, switching separately)

{c5, d5, e5}. InstsRmodel XBAR + {c4, d4, e4}.InstsRmodel SWVC +

{c3, d3, e3}.InstsRmodel InBUF + {c2, d2, e2}.InstsRmodel OutBUF +

{c1, d1, e1}.InstsRmodel CLKCTRL + {c0, d0, e0}=

{Pleaktool,Pint tool, PSW tool}

  • Multi-step regression fit
    • Step 1: Fit instances of each router component with post-layout instance counts
related work
Related work

NoC Modeling

  • Architecture templates
    • ORION2.0
  • Gate-level analytical models
  • Parametric regression
    • Pre- and post-layout power estimation
    • RTL simulations
  • Non-parametric regression
    • MARS

Circuit model

Regression model

Analytical

Arch templates

Parametric

Non-parametric

Control

ORION_NEW + regression; flit-level

Tool

  • Significant Departure: Relax the “template” mindset
results
Results
  • Avg. estimation error in # instances reduced from 109.5% to 8.8%
    • Avg. estimation error in area reduced to 9.8%
    • Avg estimation error in power reduced to 4.58%