Design for Test of Systems on Chip: Digital Test Basic Principles of Bio-Inspired Approaches to Fault Tolerance

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Design for Test of Systems on Chip Digital
TestBasic Principles of Bio-Inspired Approaches
to Fault Tolerance
TUTORIAL

• Vladimír Drábek and Lukáš Sekanina
• drabek, sekanina_at_fit.vutbr.cz
• Faculty of Information Technology
• Brno University of Technology, Czech Republic

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Tutorial outline

• Introduction
• Bio-inspired models in computer science
• Reconfigurable devices
• New trends in fault tolerance
• Cellular systems/Embryonics
• Evolvable hardware
• Immunotronics
• Conclusions

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Hardware and biology Why?

• People require powerful systems.
• These systems are complex.
• Assume 10x computing elements (x 2, 3, 6, 12,
  24)
• Adaptation to changes, self-diagnostic,
  self-repairing, self-assembling, autonomous
  control, are needed.
• Nature has a lot of experience with ...
• Now, we can use it at the HW level.

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Hardware Biology ? Three crucial factors

• Development of reconfigurable circuits
• starting with Xilinx FPGAs in 1985
• continued with reconfigurable computing
• Development of soft computing
• Goldbergs popularization of evolutionary
  algorithms
• evolutionary design (Bentley)
• THE AGE OF NANOTECHNOLOGY
• 10x computing elements
• how to ensure reliability

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10 years ago only a few people involved in the
world

• 1992 Higuchi (ETL Japan), Hugo de Garis (now
  with Utah State U.) multiplexer evolution in
  PLA
• 1993 Mange (LSL, Switzerland) self-repairing
  and self-replicating HW
• 1994 CAM (Cellular Automata Machine) Brain
  Project (de Garis)
• 1995 Thompson (U. of Sussex) intrinsic
  evolution in FPGA XC6216
• 1995 Towards Evolvable Hardware (1st
  conference, LSL, Lausanne, Switzerland)

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Nowadays conferences, journals

• Main conferences
• Evolvable systems From biology to hardware (1996
  - Japan, 1998 - Switzerland, 2000 - UK, 2001 -
  Japan, 2003 - Norway)
• NASA/DoD Workshops on Evolvable Hardware (1999,
  2000, 2001, 2002 in USA)
• Workshops on Information Processing in Cells and
  Tissues
• partially at GECCO, CEC, FPL, DDECS,
• Journals
• Genetic Programming and Evolvable Machines
• IEEE Transactions on Evolutionary Computation

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Nowadays 52 research groups(see A. Thompsons
links)

• UK 16
• USA 14
• Germany 5
• Italy 3
• Canada 2
• Japan 2
• Norway 2

• Czech R. 1
• Denmark 1
• Switzerland 1
• The Netherlands 1
• Romania 1
• Brazil 1
• Australia 1
• Mexico 1

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Resources

• Evolutionary Electronics Web Links (A. Thompson)
• http//www.cogs.susx.ac.uk/users/adrianth/EHW_grou
  ps.html
• EvoELEC at EvoNet
• http//evonet.dcs.napier.ac.uk/evoweb/working_grou
  ps/evoelec/index.html
• Reconfigurable POEtic Tissue
• http//www.poetictissue.org
• Brno University of Technology, Czech Rep.
• http//www.fee.vutbr.cz/sekanina/ehw/index.html
• The selected papers related to this tutorial
• 1 Sekanina, L., Drabek, V. Relation Between
  Fault Tolerance and Reconfiguration in Cellular
  Systems. In 6th IEEE Int. On-Line Testing
  Workshop, Palma de Mallorca, Spain, 2000, pp.
  25-30
• 2 Sekanina, L., Drabek, V. Automatic Design of
  Image Operators Using Evolvable Hardware. In 5th
  IEEE Design and Diagnostics of Electronic
  Circuits and Systems Workshop 2002, Czech Rep.,
  pp. 132-139
• 3 Sekanina, L., Drabek, V. A Survey of
  Bioinspired Methods for Design of Fault Tolerant
  Reconfigurable Architectures In BEC'02, Tallinn

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POE model for classification of bio-inspired HW
(LSL, Switzerland, 1997 )

• is adopted for classification of FT-systems in
  this presentation.
• P Phylogeny Evolution the circuit
  connection is subject of evolution (evolvable
  hardware)
• O Ontogeny Development circuit connection
  is understood as the multicellular organism
  developed from the mother cell (embryonics
  electronics)
• E Epigenesis Learning neural machines,
  immunotronics
• Combined as PO, PE, OE, POE hardware

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Some models from computer science

• O axis cellular automaton
• Von Neumann and Ulam in 1940s
• Non-uniform CA, Vichniac in 1986
• Cell Matrix, Macias in 1999
• P axis evolutionary algorithms
• Genetic algorithm - Holland in 1960s
• Evolutionary strategy Bienert, Rechenberg,
  Schwefel in 1960s
• Evolutionary programming Fogel in 1960s
• Genetic programming Koza in 1992
• E axis artificial neural network (ANN)
• McCuloch and Pitts in 1940s

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Math models and nature O-axis Cellular automaton
(CA)

• an array of simple cells
• only local interaction
• (a)synchronous operations
• uniform/nonuniform
• computational as well as
  constructional universality
• emergent computation
• Problems
• How to define rules for a given task.
• What behavior is generated using the given rules.
• Other models Lindenmayer systems

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Math models and nature P-axis Evolutionary
algorithm (EA)

• bio-inspired robust search - iterative procedure
• population of chromosomes (candidate solutions)
• Selection
• - select perspective chromosomes
• Crossover
• - exchange parts of chromosomes
• Mutation
• - change a part of the chromosome
• Fitness Calculation
• - evaluate chromosomes

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EAs continued

• Evolutionary optimization vs. evolutionary design
• Adaptation on population level
• Advantages
• provide many alternative solutions
• can generate innovative solutions
• widely applicable
• Disadvantages
• no guarantee for optimal solution within finite
  time
• weak theoretical basis
• can be computationally expensive

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Math models and nature E-axisArtificial Neural
Nets for learning

• Adaptation on individual level (learning)
• Other models the artificial immune system

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Examples of PO, OE, PE, POE

• PO cellular programming
• CA rules are evolved (Sipper).
• PE evolutionary design of ANN
• Architecture/weights/ of an ANN are evolved.
• OE development of an ANN
• ANN is built from a mother cell
• POE evolution of CA rules, CA defines the
  structure of an ANN. Then the ANN is trained.
  (CAM Brain Project, de Garis)

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Implementation platform Reconfigurable devices

• an array of programmable elements in
• ASIC
• FPGA
• Virtual reconfigurable devices in FPGAs
• Cell Matrix
• Application-given function mainly deals with P
  and E axes
• Static
• Dynamic configurable computing
• Dynamic adaptive evolvable hardware
• Re/Configuration system mainly deals with O
  axis
• Internal/External
• Partial/Full
• Controlled/Autonomous
• Serial/Parallel

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FPGA A typical structure
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New trends in fault tolerance (FT)

• O axis Cellular systems/Embryonics
• P axis Evolvable hardware
• E axis Immunotronics

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Principles of Fault Tolerance

• Hardware redundancy spare cells/columns/rows
• Always needed.
• If a cell detects a fault gt reconfiguration
• Fault-detection is needed.
• Reconfiguration scenario is CRUCIAL!!!
• 10x computing elements must be managed
  effectively.

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Cellular Systems 10x cells3 scenarios of
reconfiguration

1. Traditional approach
2. Embryology-based (Embryonics)
3. Maciass cell-based (in Cell Matrix)

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(1) FT Traditional Approach 1

• Serial configuration
• Column redundancy

• When fails Full reconfiguration is initialised.
• Slow and inefficient

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A Single Cell Implementation
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(2) FT Based on EmbryologyLevels of abstraction

• population level (virtual)
• a set of multicellular organisms
• multicellular organismic level (virtual)
• a set of cells
• cellular level (virtual)
• a set of molecules
• molecular level (basic FPGA element)
• Similar principles (fault detection, self-repair,
  self-replication, reconfiguration) are applied at
  all levels.

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Example POEtic Tissuewww.poetictissue.org
population
Hierarchical FT
organism
cell
molecule FPGA element (FT duplication/memory
testing)
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FT Cellular Division- the circuit is developed
from the mother cellCellular Differentiation-
the cell sets up its function according to
coordinates
The implementation is based on coordinate
registers placed in each cell.
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Implementation 1 Fixed coordinatesThe genome is
known at design time

• Development of the multicellular organism
• Cellular division - each cell gets entire genetic
  program (stored in Configuration Register - CR)
• Cellular differentiation - only some parts of the
  CR (given by position of the cell stored in the
  coordinate registers) define function of the cell

execution
genome
instructions
X4
Y1
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In case of a fault

• FT mechanism When a cell fails other cells
  only recalculate their positions to activate
  appropriate functions.
• Modification incomplete genom in CR
• entire system reconfiguration from an external
  device in the case of a major fault
• coordinate recalculation in the case of a minor
  fault

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Implementation 2 Relative coordinates The
genome is unknown at design time

• Opposite to the previous approach, positions of
  the cells are determined using several artificial
  diffusers (inspiration in distributed diffusers
  that release a given protein into the system)
• A cells coordinate depends on distance from the
  diffuser.
• This is to model a dynamic environment
  diffusers can change their positions dynamically.

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BioWatch projecthttp//lslwww.epfl.ch/pages/embry
onics/home.html

• BioWatch is an example of a system based on
  principles of embryology. It is a giant
  artificial organism operating as a wall watch
  that is able to self-repair in case of a minor
  fault or to self-replicate in case of major
  fault. In case of a large damage, the BioWatch
  dies.
• Implementation bio-inspired electronic wall,
  fixed coordinates, LED, XCS10XL, touch sensitive
  elements, 5000 molecules

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PO The Firefly machinehttp//lslwww.epfl.ch/page
s/research/papers/firefly/home.html

• The machine is based on the cellular programming
  approach, in which parallel cellular machines
  evolve to solve computational tasks. The firefly
  system operates with no reference to an external
  device, such as a computer that carries out
  genetic operators, thereby exhibiting online
  autonomous evolution.

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(3) Cell Matrix (N. Macias, USA)www.cellmatrix.co
m
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Maciass cell sends its configuration!

• Assume 10x cells
• A cell can send its table to the neighboring
  cells!
• Internal, distributed reconfiguration

• if C0 gt asynchronous data mode
• if one of C1 gt synchronous configuration mode

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Distributed and internal reconfiguration of HW
Cell X configures cell Z by first configuring
cell Y to act as a router (with table T1) and
then passing table T2 into Z via Y. That can be
done in parallel in many regions.
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Example An expanding adderIf overflows then
build new stage autonomously!
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Potential applications(the results are from
simulators, only an 8x8 cell chip exists)

• Cell Matrix is a platform for nanocomputing.
• 56bit DES cracker at 256 Kbaud (1023 cells)
• DNA sequence alignment
• Image processing
• Self-assembling and self-repairing circuits for
  space applications
• Supercell 72900 cells (270x270), implements a
  single two-input, one-output functional block
• Supercells find defect-free regions.
• Supercells copy correct circuits into these
  regions.
• Supercells work autonomously after a self-test
  command is supplied.

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New trends in FT

• O axis Cellular systems/Embryonics
• P axis Evolvable hardware
• E axis Immunotronics

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Evolvable hardwareEHW EA reconfigurable HW
The circuit connection is encoded in the
chromosome. Fitness correct outputs for all
input combinations (in case of small
combinational circuits)
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Example Image filter design 2
original image
corrupted image (Gaussian noise)
chromosome
filtered image
Digital circuit
fitness
Comparator
N x N pixels, N 256
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Evolutionary design of shot noise filtersSome
results
Median filter 4740 gates
RA3P5 1702 gates
F57 441 gates
IF-THEN-ELSE 123 gates
shot noise
QUALITY
Conventional filters
Evolved filters
HW COST
Another circuits evolved Gaussian noise filters,
edge detectors
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An evolved circuit (edge detector)
Intron A and A A
Redundant (inactive) elements
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Redundancy and inherent FT

• Redundancy is beneficial for HW evolution.
• Mutations can be considered as faults.
• Inherent FT A perfect circuit could appear in a
  few generations after a fault (because of
  redundancy).
• Neutral mutation does not change fitness of the
  circuit ( inherent FT).
• Intron a part of chromosome which does not
  affect the fitness.
• Rigidity of the circuit the evolved circuit
  depends on mutations only minimally.

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Redundancy of encoding improves FT
110
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Explicit FT in evolvable hardware

• The requirements for FT are included into the
  fitness function (e.g. some critical cases are
  tested during evaluation of the circuit).
• Disadvantage time consuming fitness calculation

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New trends in FT

• O axis Cellular systems/Embryonics
• P axis Evolvable hardware
• E axis Immunotronics

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Immunotronics immunological electronics

• The immune system
• recognizes all cells (or molecules) within the
  body and categorizes those cells as self or
  nonself.
• From an engineering viewpoint it is a
  multi-layer, parallel and distributed adaptive
  system that uses learning and memory to perform
  pattern recognition task in a decentralized
  fashion.

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Immunotronics as FSM (Univ. of York, 2001)

• The automaton of the system consists of the valid
  and invalid states and transitions allowing
  extraction self and nonself conditions required
  for fault detection. Faults can be detected by
  monitoring of the transitions.
• The hardware immune system is created in four
  steps
• A test bench is used to collect self data from
  the finite state machine undergoing the
  immunisation process.
• A set of tolerance conditions is extracted from
  self data.
• The selected tolerance conditions are then
  downloaded into the hardware immune system.
• During operation, the inputs and current state of
  the finite state machine are extracted and passed
  through to the immune system. The immune system
  searches through all tolerance conditions at the
  same time to determine the validity of the
  extracted string. If a match is found then a
  potential fault is indicated.

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Bradley and Tyrrells schema (ICES01)
Hardware enclosure
Environmental control
complexity
Hardware immune system
Immune system control
Protected system
Memory tolerance conditions
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Conclusions

• Bio-inspired FT new, topical but starting branch
  which has entered into HW design.
• We have to add some redundancy in all cases.
  Bio-inspired approaches should exploit this
  redundancy in much better way than engineers
  usually do.
• Practical results in industrial applications
  open problem nowadays.
• We are waiting for suitable reconfigurable
  platforms. Maybe nanotechnology?