Expediting GABased Evolution Using Group Testing Techniques for Reconfigurable Hardware1

1
Expediting GA-Based Evolution Using Group Testing
Techniques for Reconfigurable Hardware1
ReConFig06San Luis Potosi - Mexico
Rashad S. Oreifej, Carthik A. Sharma, and Ronald
F. DeMaraUniversity of Central Florida
1. Research support in-part by NSF grant CRCD
0203446
2
Evolvable Hardware

• Evolutionary Design
• Start with available CLBs and IOBs
• Implement a design using Genetic Operators etc
  Fogarty97
• Limited or no ability to re-design to account for
  suspected faulty resources

• Evolutionary Regeneration
• Start with an existing pool of designs
• Some existing configurations may use faulty
  resources
• Eliminate use of suspected faulty resources
• Genetic Operators can be applied to refurbish
  designs Vigander01

3
Previous Work

• Pre-compiled Column-Based Dual FPGA architecture
  Mitra04
• Autonomous detection, repair by shifting
  pre-compiled columns
• Isolation using distributed CED-checkers and
  blind reconfiguration attempts
• Overview of Combinatorial Group Testing and
  Applications Du00
• Provides taxonomy and general algorithms for
  applying CGT
• Examples of CGT applications DNA clone library
  filtering, vaccine screening, computer fault
  diagnosis, etc.
• CGT Enhanced Circuit Diagnosis Kahng04
• Present doubling, halving etc for circuit fault
  diagnosis using BIST, CGT
• Requires ability to test resources individually
• Chinese Remainder Sieve technique Eppstein05
• Efficient non-adaptive and two-stage CGT based on
  prime number driven test formation
• Improved algorithms for practical problem sizes
  (n lt 1080) with small number of defectives (d lt
  4)

4
Genetic Algorithms Evolvable Hardware

• GAs are strong candidates for implementing
  system refurbishment
• They implement guided trial-and-error search
  using principles of Darwinian evolution
• Iterative selection enforces survival of the
  fittest
• Genetic operators - mutation, crossover, - can
  be used to refurbish designs
• Hypothesis Information regarding resource
  performance can expedite GA-based refurbishment

• GAs frequently use strings of 1s and 0s to
  represent candidate solutions
• FPGA Configuration File is a String of 1s and 0s

5
Conventional vs. CGT-Pruned GA

• Conventional GA Searches the whole space to
  evolve a working design or repair
• Information about resource suitability may
  accelerate search
• CGT-Pruned GA Prefers resources of higher
  fitness to evolve a working design or repair.
• Q. How to obtain resource fitness information?
• A. Using Group Testing Techniques.
• Combinatorial Group Testing identifies a
  decreasing group of defectives by iterative
  refinement
• Tests on subsets of suspects
• Is expected to take less time. Faster Design and
  Faster Repair

?
6
CGT-Pruned GA Simulator
7
Experimental Setup
8
CGT-Pruned Refurbishment

• Isolate and Avoid suspect resources from being
  used

• Hypothesis
• CGT-Pruned GA Repair evolves a full fitness
  circuit faster than Conventional GA Repair
• Results show performance improvement in
  CGT-Pruned Repair

9
Results Conventional Vs. CGT-Pruned Repair
10
Achieving Refurbishment with Cell Swapping

• Isolate and Swap suspect resources
• Cell Swapping Operator
• Copy suspect resource Cell configuration to
  another unused cell
• GA searches for routing strategy to re-route
  interconnect to the previously-unused cell
• Refurbishment with Cell Swapping
• Swap suspect cells one by one and evaluate
  fitness until full fitness is evolved
• If swapping all suspect cells does not realize
  complete refurbishment, then employ other GA
  operators

11
Repair Progress
12
CGT-Pruned GA Design

• Evolve the entire circuit design from scratch
• Avoid suspect resources and take advantage of
  resource redundancy within the FPGA

• CGT-Pruning outperforms Conventional GA-based
  techniques

13
Results Conventional Vs. CGT-Pruned Design
14
Comparison of Performance Number of
Generations for Repair

• More than 70 of the experiments benefited
  substantially from resource information generated
  using CGT

15
Results Summary

• As opposed to Conventional GAs, CGT-Pruned GAs
• Completely refurbish configurations in 38 fewer
  generations
• Design fully functional configurations in 16
  fewer generations
• Faulty resources are eliminated from
• Pool of unused-resources in the case of repair as
  opposed to the pool of all-resources in the case
  of design.
• Repair complexity vs. Design complexity
• Repair complexity ltlt Design complexity
• Repairs were realized in one-fifth of the time
  required for Design

16
Backup Slides

• On following pages

17
Motivation

• Mission-critical Embedded Systems require high
  reliability and availability
• Characteristics of Operating Environment may
  induce hardware failures
• Aging, Manufacturing Defects, etc.
• System Reliability
• Fault Avoidance. Always Possible? No
• Design Margin. Always Adequate? No
• Modular Redundancy. Always Recoverable?No
• Fault Refurbishment. Highly Flexible? Yes
  but technically challenging to achieve

?
18
Group Testing Techniques
H i,j

• Competitive Group Testing
• Algorithm based on group testing methods
• Use competition between configurations
• Temporal information stored in H matrix
• Successive intersection
• Monitor health history of resources which
  presents resource fitness
• Simulated using C programming language and GSL
  functions Sharma-06

?i,j
Relative fitness of resource a 1/H i,j
19
Three Fast Runs of the CGT-pruned GA Repair

• GA evolves to a relatively very high fitness
  within the first few hundreds generations, but
  takes significantly more generations to reach the
  maximum fitness

20
References

• 1 Fogarty T. C., J. F. Miller, and P. Thomson,
  "Evolving Digital Logic Circuits on Xilinx 6000
  Family FPGAs," in Proceedings of The 2nd Online
  Conference on Soft Computing, 23-27 June 1997.
• 2 Sverre Vigander, Evolutionary Fault Repair
  in Space Applications, Masters Thesis, Dept. of
  Computer Information Science, Norwegian
  University of Science and Technology (NTNU),
  Trondheim, 2001.
• 3 C. A. Sharma, R. F. DeMara, "A Combinatorial
  Group Testing Method for FPGA Fault Location",
  accepted to International Conference on Advances
  in Computer Science and Technology (ACST 2006),
  Puerto Vallarta, Mexico, January 23 - 25, 2006
• 4 S. Mitra and E. J. McCluskey, Which
  Concurrent Error Detection Scheme to Choose?, in
  Proceedings of the International Test Conference
  2000, p. 985, October 2000.
• 5 D. Du and F. K. Hwang. Combinatorial Group
  Testing and its Applications, volume 12 of Series
  on Applied Mathematics. World Scientific, 2000.
• 6 A. B. Kahng and S. Reda. Combinatorial Group
  Testing Methods for the BIST Diagnosis Problem,
  in Proceedings of the Asia and South Pacific
  Design Automation Conference, January 2004.
• 7 Keymeulen, D. Zebulum, R.S. Jin, Y.
  Stoica, A.. Fault-Tolerant Evolvable Hardware
  Using Field-Programmable Transistor Arrays, IEEE
  Transactions On Reliability, Vol. 49, No. 3,
  September 2000
• 8 Lohn, J. Larchev, G. DeMara, R.
  Evolutionary fault recovery in a Virtex FPGA
  using a representation that incorporates
  routing, Parallel and Distributed Processing
  Symposium, 2003. Proceedings. International 22-26
  April 2003
• 9 Lach, J. Mangione-Smith, W.H. Potkonjak, M.
  Low overhead fault-tolerant FPGA systems, Very
  Large Scale Integration (VLSI) Systems, IEEE
  Transactions on Volume 6,  Issue 2,  June 1998
• 10 Miron Abramovici, John M. Emmert and Charles
  E. Stroud , Roving Stars An Integrated Approach
  To On-Line Testing, Diagnosis, And Fault
  Tolerance For Fpgas In Adaptive Computing
  Systems, The Third NASA/DoD Workshop on
  Evolvable Hardware, Long Beach, Cailfornia 2001

21
Previous Work

• Fault Tolerant Design and Detection
  Characteristics

Incorporates resource performance information
22
Previous Work

• Fault Recovery Characteristics

23
Our Goal Autonomous FPGA Refurbishment
increase availability without carrying
pre-configured spares

• Redundancy
• increases with amount
• of spare capacity
• restricted at design-time
• based on time required to select spare
  resource
• determined by adequacy of spares available (?)
• yes

• Refurbishment
• weakly-related to number
• recovery capacity
• variable at recovery-time
• based on time required to find suitable
  recovery
• affected by multiple characteristics (
  or -)
• yes

everyday example
spare tires
can of fix-a-flat
?
Overhead from Unutilized Spares weight, size,
power Granularity of Fault Coverage
resolution where fault handled
Fault-Resolution Latency availability via
downtime required to handle fault Quality
of Repair likelihood and completeness
Autonomous Operation fix without outside
intervention
?
?
?
?
?
24
GA Success Stories

• Commercial Applications
• Nextel frequency allocation for cellular phone
  networks -- 15M predicted savings in
  NY market
• Pratt Whitney turbine engine design ---
  engineer 8 weeks
  GA 2 days w/3x improvement
• International Truck production scheduling
  improved by 90 in 5 plants
• NASA superior Jupiter trajectory optimization,
  antennas, FPGAs
• Koza 25 instances showing human-competitive
  performance such as analog circuit design,
  amplifiers, filters

25
Adaptive GA Design
Arithmetic mean for twenty experiments
Standard Deviation for twenty experiments
26
Analysis Metrics
27
CGT-Pruned GA Simulator

• C based console application
• Consists of
• Combinatorial Group Testing component
• Uses Gnu Scientific Library (GSL)
• Genetic Algorithm component
• Object oriented architecture that models FPGA
  resources
• Modes of Operation
• CGT-Pruned GA Repair
• Use CGT to isolate suspect resources
• Avoid use of suspect-faulty resource in design
  refurbishment process
• CGT-Pruned GA Repair with Cell Swapping
• Swap suspect-faulty resources with previously
  unused resources to evolve a recovery
• CGT-Pruned GA Design
• Evolve a new working design while avoiding
  suspect resources