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Paintable Computing: Bio-Inspired Self-Assembly Architecture Presentation

Explore the concept of "paintable computing," where particles are suspended in a viscous medium and deposited on surfaces to create computing elements. Learn about self-assembly, system architecture, process fragments, applications, and more in bio-inspired computing. Discover the characteristics, motivations, difficulties, and comparisons with SensorNets in this innovative approach. Gain insights into hardware platforms, memory organization, interactions, self-assembly categories, and unique pFrag behaviors. Uncover the concepts of BreadCrumb pFrag, NearSightedMailMan, Gradient pFrag, MultiGrad, Tessellation Operator, Channel Operator, Coordination Operator, and Diffusion in this cutting-edge computational paradigm.

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Paintable Computing: Bio-Inspired Self-Assembly Architecture Presentation

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  1. Paintable Computer Ting Yan CS 851 Bio-Inspired Computing Presentation March 25, 2003

  2. Butera’s Dissertation • Introduction • Background - Cost Analysis, Self-Assembly • System Architecture - HW, PM, Simulator • Essential Process Fragments • Applications • Wrap-Up

  3. What is a paintable? • … particles … suspended into a viscous medium and deposited it on surfaces like paint

  4. Characteristics • Sand size, limited resource • Ability to harvest power from environment • Arbitrary topology, no localization • Wireless local communication • Single particle failure • Asynchrony

  5. Motivation and Difficulty • Economics • Computing power for a whole wafer constant • The larger the dies, the lower the yield • Cost-effective to use dense ensembles of dust size computing elements instead of centralized architectures • Difficult for people to structure • If we can not get a human to structure the procedures, we are going to have to get the procedures to structure themselves. • Self-Assembly, Autonomic Computing, e.g., self-organization, self-management

  6. Comparison with SensorNets • Sizes - dust-size vs. coin-size • Power - environment harvesting vs. battery • Purpose - computing vs. computing + sensing + actuating

  7. Hardware Platform

  8. Memory Organization

  9. Self-contained Executables

  10. Interactions • pFrags read/write tagged data from/to homepage • When a pFrag posts tagged data to the homepage of its own particle, copies of the post appear at all mirror sites • pFrags propagate and migrate among particles • Errors, packet losses should be handled

  11. Self-Assembly • Categories • Scaffolded: shape lock-and-key • Thermodynamic: minimum free energy • Code: guided by coded instructions • Arbitrarily complex system behavior can be created from large numbers of simple processing elements (pFrags). • Global reliable computation can be obtained from aggregate statistics on a large set of local interactions.

  12. BreadCrumb pFrag • Purpose - monotonically ascending addresses • Update behaviors • propagation, adaptation or removal

  13. NearSightedMailMan • Purpose - routing • based on BreadCrumb • by HomePage posts

  14. Gradient pFrag • Basically, hop counts from a external device • Stages • installation, propagation, adaptation, removal • Adaptation formula

  15. Gradient Effect • When stabilized, HC is the minimum hop count to the reference point • Common problems: How long does it take? Race conditions? pFrag always takes place in memory

  16. Gradient Adaptation

  17. Get Location with Gradient Precision proportional to communication radius, affected by node density.

  18. MultiGrad - vFrag - One virtual pFrag emulating multiple pFrags - Save memory space - Any pFrag can issue a request for Gradient

  19. Tessellation Operator • Purpose: group the particles into the Voronoi regions about a uniformly distributed set of anchor points • MultiGrads used to obtain distance to a certain particle • Centroid - minimize potential energy for a spring force like field

  20. Tessellation - Details

  21. Tessellation - Issues • Time issues - settling time, randomness, large moves • Precision • Initial field strength - neither too low nor too high would work

  22. Tessellation Adaptation

  23. Channel Operator

  24. Channel Operator • End-to-End communication • Gradient, Tracers and Halos • Gradient issued at the destination • Gradient - a waste of bandwidth? • Cross-traffic prohibited?

  25. Coordination Operator

  26. Coordination - Example

  27. Diffusion • Diffuse a stream of data “fairly” in the ensemble - time and space • Rule - the pFrag with the maximum Timer count searches the I/O space for the neighboring particle with the smallest number of Diffusion posts.

  28. Diffusion - Result

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