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Evolving, Adaptable Visual Processing System Simon Fung-Kee-Fung
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Background Papers “Lucian Prodan, Gianluca Tempesti, Daniel Mange, André Stauffer, “Biology Meets Electronics: The Path to a Bio- Inspired FPGA”, Proceedings from the 3 rd 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
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Plan Introduction Embryonics Project Conclusions of Authors Relevance to our Project Advantages of Evolvable Hardware
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Introduction Adaptive Machines –Plasticity –Vs. Conventional Computer Hardware
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Evolvable Hardware Used in development of on-line adaptive machines An example: Embryonics Project
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Embryonics Project Embryonics = Embryo + Electronics Goals –Similarity –Effectiveness Ontogenesis: the development of a single organism from a single cell to an adult.
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Fundamental Features Multicellular organization Cellular Division Cellular Differentiation
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Artificial Cells Simple Processor Set of Instructions Functionality = Parallel operation
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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
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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
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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
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Self Repair Cellular Level - Each cell stores the entire genome Molecular Level – All molecules are identical
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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
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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
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Benefits of Evolvable Hardware Run-time reconfigurability Higher performance than general- purpose processors More flexible than ASICs Customization
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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
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