A System for Automatic Recording and Prediction of Design Quality Metrics

1
A System for Automatic Recording and Prediction
of Design Quality Metrics

• Andrew B. Kahng and Stefanus Mantik
• UCSD CSE and ECE Depts., La Jolla, CA
• UCLA CS Dept., Los Angeles, CA

2
Introduction

• Time-to-market window is shrinking very rapidly
• Product quality and design process must
  continually improve
• Currently, there are no standards or
  infrastructure for measuring and recording the
  semiconductor design process
• METRICS provides
• Standard infrastructure for the collection and
  the storage of design process information
• Standard list of design metrics and process
  metrics
• Analyses and reports that are useful for design
  process optimization
• METRICS allows Collect, Data-Mine, Measure,
  Diagnose, then Improve

3
Related Works

• Enterprise- and project-level metrics (Numetrics
  Complexity Unit) Numetrics Management Systems
  DPMS
• Other in-house data collection systems
• e.g., TI (DAC 96 BOF), OxSigen LLC (Siemens
  Semiconductor)
• Design Process Management Jacome93, Brockman92,
  Johnson96
• Web-based design support
• IPSymphony, WELD, VELA, etc.
• Continuous process improvement
• Data mining and visualization

4
Outline

• METRICS system architecture and standards
• METRICS for design flow
• Flow METRICS experiments
• METRICS integration with datamining
• Datamining integration experiments
• Issues and conclusions

5
METRICS System Architecture
6
Generic and Specific Tool Metrics
Partial list of metrics now being collected in
Oracle8i
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Outline

• METRICS system architecture and standards
• METRICS for design flow
• Flow METRICS experiments
• METRICS integration with datamining
• Datamining integration experiments
• Issues and conclusions

8
Flow Metrics

• Tool metrics alone are not enough
• Design process consists of more than one tool
• A given tool can be run multiple times
• Design quality depends on the design flow and
  methodology (the order of the tools and the
  iteration within the flow)
• Flow definition
• Directed graph G (V,E)
• V ? T ? S, F
• T ? T1, T2, T3, , Tn (a set of tasks)
• S ? starting node, F ? ending node
• E ? Es1, E11, E12, , Exy (a set of edges)
• Exy
• x lt y ? forward path
• x y ? self-loop
• x gt y ? backward path

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Flow Example
S
T1
T2
T3
Optional task
T4
F
Task sequence T1, T2, T1, T2, T3, T3, T3, T4,
T2, T1, T2, T4
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Flow Tracking
Task sequence T1, T2, T1, T2, T3, T3, T3, T4,
T2, T1, T2, T4
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NELSIS Flow Manager Integration

• Flow managed by NELSIS

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Optimization of Incremental Multilevel FM
Partitioning

• Motivation Incremental Netlist Partitioning
• netlist ECOs are made want top-down placement
  to remain similar to previous result
• good approach CaldwellKM00 V-cycling based
  multilevel Fiduccia-Mattheyses
• what is the best tuning of the approach for a
  given instance?
• break up the ECO perturbation into multiple
  smaller perturbations?
• starts of the partitioner?
• within a specified CPU budget?

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Optimization of Incremental Multilevel FM
Partitioning (contd.)

• Given initial partitioning solution, CPU budget
  and instance perturbations (?I)
• Find number of parts of incremental partitioning
  and number of starts
• Ti incremental multilevel FM partitioning
• Self-loop ? multistart
• n ? number of breakups (?I ?1 ?2 ?3 ...
  ?n)

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Multilevel FM Experiment Flow Setup

• foreach testcase
• foreach ?I
• foreach CPUbudget
• foreach breakup
• Icurrent Iinitial
• Scurrent Sinitial
• for i 1 to n
• Inext Icurrent ?i
• run incremental multilevel FM partitioner
• on Inext to produce Snext
• if CPUcurrent gt CPUbudget then break
• Icurrent Inext
• Scurrent Snext
• end

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Flow Optimization Results

• If (27401 lt num edges ? 34826) and (143.09 lt cpu
  time ? 165.28) and (perturbation delta ? 0.1)
  then num_inc_parts 4 and num_starts 3
• If (27401 lt num edges ? 34826) and (85.27 lt cpu
  time ? 143.09) and (perturbation delta ? 0.1)
  then num_inc_parts 2 and num_starts 1
• ...

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Identifying the Effect of Wire Load Model

• Wire load model (WLM) is used for pre-layout
  estimation of wire delays
• Three different WLMs
• statistical WLM
• structural WLM
• custom WLM
• Motivation
• identify if WLMs are useful for estimation
• identify if WLMs are necessary for optimization
• identify the best role of WLMs

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Wire Load Model Flow

• WLM flows for finding the appropriate role of WLM
• T1 synthesis technology mapping
• T2 load wire load model (WLM)
• T3 pre-placement optimization
• T4 placement
• T5 post-placement optimization
• T6 global routing
• T7 final routing
• T8 custom WLM generation

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WLM Experiment Setup

• foreach testcase
• foreach WLM (statistical, structural, custom,
  and no WLM)
• foreach flow variant
• run PKS flow
• if WLM structural then
• generate custom WLM
• end

6 different flow variants
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WLM Flow Results
Slack comparison for 6 flow variants

• Post-placement and pre-placement optimizations
  are important steps
• Choice of WLM depends on the design
• WLMs are still useful

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Outline

• METRICS system architecture and standards
• METRICS for design flow
• Flow METRICS experiments
• METRICS integration with datamining
• Datamining integration experiments
• Issues and conclusions

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Datamining Integration
Inter-/Intranet
DM Requests
SQL
Results
Tables
Database
Datamining Interface
Datamining Tool(s)
Tables
Tables
SQL
Results
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Categories of Data for DataMining

• Design instances and design parameters
• attributes and metrics of the design instances
• e.g., number of gates, target clock frequency,
  number of metal layers, etc.
• CAD tools and invocation options
• list of tools and user options that are available
• e.g., tool version, optimism level, timing driven
  option, etc.
• Design solutions and result qualities
• qualities of the solutions obtained from given
  tools and design instances
• e.g., number of timing violations, total tool
  runtime, layout area, etc.

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Possible Usage of DataMining

• Design instances and design parameters
• CAD tools and invocation options
• Design solutions and result qualities
• Given ? and ?, estimate the expected quality of ?
• e.g., runtime predictions, wirelength
  estimations, etc.
• Given ? and ?, find the appropriate setting of ?
• e.g., best value for a specific option, etc.
• Given ? and ?, identify the subspace of ? that is
  doable for the tool
• e.g., category of designs that are suitable for
  the given tools, etc.

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Example Applications with DM

• Parameter sensitivity analysis
• input parameters that have the most impact on
  results
• Field of use analysis
• limits at which the tool will break
• tool sweet spots at which the tool will give best
  results
• Process monitoring
• identify possible failure in the process (e.g.,
  timing constraints are too tight, row utilization
  is too high, etc.)
• Resource monitoring
• analysis of resource demands (e.g., disk space,
  memory, etc.)

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DM Results QPlace CPU Time

• If (num nets ? 7332) then CPU time 21.9
  0.0019 num cells 0.0005 num nets 0.07 num
  pads - 0.0002 num fixed cells
• If (num overlap layers 0) and (num cells ?
  71413) and (TD routing option false) then CPU
  time -15.6 0.0888 num nets - 0.0559 num cells
  - 0.0015 num fixed cells - num routing layer
• ...

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Selection for Training and Test Sets

• Random Case
• randomly select runs assigned to training set
• leave all remained (unselected) runs for test set
• Distinct Case
• split the test cases into two distinct sets, the
  training set and the test set
• assign the runs accordingly
• Representative Case
• split the test cases into two distinct sets and
  assign the runs accordingly
• for each test case in the test set, move exactly
  one run to the training set
• I.e., for each test case, there is at least one
  representative run in the training set

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Prediction Result Variances
Random Case
Distinct Case
Representative Case
Prediction of QP Wirelength
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CTGen Results
Max Insertion Delay (ns)
Min Insertion Delay (ns)
Max Skew
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Outline

• METRICS system architecture and standards
• METRICS for design flow
• Flow METRICS experiments
• METRICS integration with datamining
• Datamining integration experiments
• Issues and conclusions

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Conclusions

• Extensions to current METRICS system is presented
• Complete prototype of METRICS system is working
  at UCLA with Oracle8i, Java Servlet and XML
  (other working prototypes are installed at Intel
  and Cadence)
• METRICS wrappers for Cadence, Synopsys and UCLA
  tools and flows
• METRICS system is integrated with Cubist
  datamining tool and NELSIS flow manager
• A complete METRICS system can be installed on a
  laptop and configured to work behind firewalls

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Issues and Ongoing Work

• Issues for METRICS constituencies to solve
• security proprietary and confidential
  information
• standardization flow, terminology, data
  management, etc.
• social big brother, collection of social
  metrics, etc.
• Ongoing work with EDA, designer communities to
  identify tool metrics of interest
• users metrics needed for design process
  insight, optimization
• vendors implementation of the metrics
  requested, with standardized naming / semantics

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Thank You
http//vlsicad.cs.ucla.edu/GSRC/METRICS