Evolving, Adaptable Visual Processing System Simon Fung-Kee-Fung.
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Transcript of Evolving, Adaptable Visual Processing System Simon Fung-Kee-Fung.
Evolving, Adaptable Visual Processing System
Simon Fung-Kee-Fung
Background Papers• Lucian Prodan, Gianluca Tempesti, Daniel Mange, André
Stauffer, ““Biology Meets Electronics: The Path to a Bio-Inspired FPGA” , Proceedings from the 3rd International Conference on Evolvable Systems: From Biology to Hardware, pp 189 –196, Springer Verlag 2000
• Also:– T. Higuchi, M. Iwata, Isamu Kajutani, Hitoshi Iba, Yuji Hirao,
Tatsumi Furuya, Bernard Manderick, “Evolvable Hardware and Its Applications to Pattern Recognition and Fault-Tolerant Systems” , Towards Evolvable Hardware: The Evolutionary Engineering Approach, pp118-135, Springer Verlag 1996
Plan
• Introduction• Embryonics Project• Conclusions of Authors• Relevance to our Project• Advantages of Evolvable Hardware
Introduction
• Adaptive Machines– Plasticity
– Vs. Conventional Computer Hardware
Evolvable Hardware
• Used in development of on-line adaptive machines
• An example: Embryonics Project
Embryonics Project
• Embryonics = Embryo + Electronics• Goals
– Similarity – Effectiveness
• Ontogenesis: the development of a single organism from a single cell to an adult.
Fundamental Features
• Multicellular organization
• Cellular Division
• Cellular Differentiation
MUX
COMP
MUX
dMOLCODE
MOLECULE
ORG ORG
ORG ORGPopulation level
(population = organisms)
Organismic level (organism = cells)
Cellular level (cell = molecules)
Molecular level (basic FPGA's element)
c
b
a d
e
f
A C E
B D F
RG: ribosomic genome PG: polymerase genome
OG: operative genome ORG
CELL
Artificial Cells
• Simple Processor• Set of Instructions• Functionality = Parallel operation
Cyclic vs. Addressable Memory Implementation
• Each cell stores the entire genome • Conventional Addressable Memory
– relatively complex addressing and decoding logic
– Contrary to requirement that cells be as simple as possible
Cyclic Memory• In living cells, the genetic information is
processed sequentially• CM does not require any addressing• Data access is similar to how the
ribosome processes the genome in a living cell
Artificial Molecules
• FPGA – a two-dimensional array of programmable logic elements
• Uniform surface of of programmable elements (our molecules)
• Can be assigned a function at runtime via a software configuration
Self RepairCellular Level - Each cell stores the entire genome
Molecular Level – All molecules are identical
Conclusions
• Programmable circuits necessary– Need to vary the cellular structure as a
function of the application.– Need to efficiently store the important
amount of memory required by a genome-based approach
Relevance
• Image analysis by FPGAs– Break down using multi-level approach– Each section represents a receptive field
• Edge Detection– More complex = smaller receptive field– Smaller receptive field = more cells/area
• System needs to adapt to real-time video
Benefits of Evolvable Hardware
• Run-time reconfigurability• Higher performance than general-
purpose processors• More flexible than ASICs• Customization
THE BIG PICTURE
• Establish a model of the retina• Devise a system that can be used to
help certain people with visual impairments see better