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 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, pp , 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

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 Repair Cellular 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