Forensic Engineering Techniques for VLSI CAD Tools

1
Forensic Engineering Techniques for VLSI CAD
Tools

• Jennifer L. Wong,
• Darko Kirovski, David Liu, Miodrag Potkonjak

UCLA Computer Science Department University of
California, Los Angeles June 8, 2000
wongjen_at_cs.ucla.edu
2
Computational Forensic Techniques

• Motivation
• Related Work
• Objectives and Applications

• Conclusion

• Generic Approach
• Graph Coloring
• SAT

3
Motivation

• Economic Impetus
• Cadence vs. Avant!
• Symantec vs. McAfee
• Research Challenge
• Applications

4
Elementary, My Dear Watson
Black Box 1
Black Box 2
5
Forensics101

• Problem Inside a room there are
• 3 light bulbs
• outside the room there are 3 switches.
• Find which switch corresponds to which light bulb
  by only entering the room once?

OFF
OFF
OFF
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Forensics101

• Solution Turn switch A on for a long time. Then
  turn it off and turn switch B on. Enter the room,
  the light that is on corresponds to switch B, the
  light bulb which is hot corresponds to switch A,
  and the last light bulb goes to switch C.

C
B
ON
A
OFF
ON
Before one can conclude anything, one must have
the right data.
7
Related Work Lucy

• Donald Johanson Tom Gray
• 1974 Ethiopia
• 3.18 million year old skeleton
• 40 of a single Hominid skeleton
• 1/3 the size of the brain of a human
• Structure of bones show upright stance
• Missing link to evolution

8
Related Work DNA

• 1944 Oswald Avery
• 1953 James Watson Francis Crick
• 1984 Sir Alec Jeffreys
• 1986 Kary Mullis

9
Related Work Shakespeare

• Thisted Efron Test
• How many new words would Shakespeare use if he
  were to write another play?
• Shall I Die?
• 900,000 word vocabulary
• 14,378 used once 7 /- 3 words 9
• 4,343 used twice 4 /- 2 words 7
• 2,292 three times 3 /- 2 words 5

10
Related Work Rembrandt

• Authenticity of work
• Panels, Canvases Ground
• Copies

Edge of a seventeenth-century oak panel.
Old Man with gorget and black cap, c. 1631.
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Related Work

• B.S. Baker U. Manber
• 98 Java Byte Codes
• Watermarking - UCLA, Charbon, Oliviera
• Collberg Thomborson
• 99 Software Protection
• M. Kuhn R. Anderson
• 97 Reverse Engineering
• Mayer (rebuttal Kuhn)

12
Forensic Engineering Applications

• Intellectual Property Protection
• Efficient Algorithm Selection
• Algorithm Tuning
• Instance Partitioning
• Benchmark Selection
• Mobile Code Protection

13
Objectives

• Strategic Goal Develop theory and tools for
  Computational Forensic Engineering.

Practical Goal Given a design, find which
tool was used to produce it.
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Forensic Engineering Generic Approach
Original Problem Instance
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Forensic Engineering Generic Approach
P2
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P1
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Pattern and Statistics Data Collection

• Identify Relevant Properties
• Quantify Relevant Properties
• Establish Their Real Relevance
• Develop Fast Algorithm for Property Extraction

17
Clustering of Algorithms and Decision Making

• Positioning in n-dimensional space
• NP-complete problem
• Nonparametric Statistical Techniques
• Estimation and Validation Techniques

18
Graph Coloring Solvers

• GC Problem
• Instance GV,E
• Solution A coloring of all vertices, with a
  minimum number of colors, in the graph in such a
  way that no 2 vertices with an edge between them
  is colored the same color

• GC Solvers
• SEQ (sequential)
• DSATUR ( Brelaz)
• RLF ( Leighton)

• GC Solvers
• SEQ (sequential)
• DSATUR ( Brelaz)
• RLF ( Leighton)

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Graph Coloring Solvers

• GC Problem
• Instance GV,E
• Solution A coloring of all vertices, with a
  minimum number of colors, in the graph in such a
  way that no 2 vertices with an edge between them
  is colored the same color

• Graph Coloring Solvers
• SEQ (sequential )
• DSATUR ( Brelaz)
• RLF ( Leighton)

• Graph Coloring Solvers
• SEQ (sequential)
• DSATUR ( Brelaz)
• RLF ( Leighton)

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Graph Coloring Properties

• Color Class Cardinality
• Sum of Degrees of Nodes included in the Largest
  (Smallest) Color Classes
• Percent of Maximal Independent Subsets

21
Boolean Satisfiability Solvers
V v1, v2, v3 C v1, v2, v1, v1, v3,
v1, v2, v3, v3
V v1, v2, v3 C v1, v2, v1, v1, v3,
v1, v2, v3, v3 Solution v1
False v2 True v3 True

• Satisfiability Problem
• Instance A set of variables V and a
  collection C of clauses over V.
• Solution A truth assignment for V such that
  at least one variable in each clause evaluates to
  true.

• Satisfiability Solvers
• GSAT (Selman)
• WalkSAT (Selman)
• NTAB (Crawford)
• Rel_SAT_rand (Bayardi and Schrag)

• Satisfiability Solvers
• GSAT (Selman)
• WalkSAT (Selman)
• NTAB (Crawford)
• Rel_SAT_rand (Bayardi and Schrag)

22
Boolean Satisfiability Properties

• Percentage of Non-Important Variables
• Clausal Stability - of variables that can
  switch their assignments such that K of clauses
  in C are still satisfied
• Ratio of true assigned variables vs. total number
  of variables in a clause

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Experimental Results Boolean Satisfiability -
of Non-Important Variables
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Experimental Results Boolean Satisfiability-Rati
o of True Variables
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Experimental Results Boolean Satisfiability-Clau
sal Stability
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Conclusion