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Paintable Computer. Ting Yan CS 851 Bio-Inspired Computing Presentation March 25, 2003. Butera’s Dissertation. Introduction Background - Cost Analysis, Self-Assembly System Architecture - HW, PM, Simulator Essential Process Fragments Applications Wrap-Up. What is a paintable ?.
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Paintable Computer Ting Yan CS 851 Bio-Inspired Computing Presentation March 25, 2003
Butera’s Dissertation • Introduction • Background - Cost Analysis, Self-Assembly • System Architecture - HW, PM, Simulator • Essential Process Fragments • Applications • Wrap-Up
What is a paintable? • … particles … suspended into a viscous medium and deposited it on surfaces like paint
Characteristics • Sand size, limited resource • Ability to harvest power from environment • Arbitrary topology, no localization • Wireless local communication • Single particle failure • Asynchrony
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
Comparison with SensorNets • Sizes - dust-size vs. coin-size • Power - environment harvesting vs. battery • Purpose - computing vs. computing + sensing + actuating
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
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.
BreadCrumb pFrag • Purpose - monotonically ascending addresses • Update behaviors • propagation, adaptation or removal
NearSightedMailMan • Purpose - routing • based on BreadCrumb • by HomePage posts
Gradient pFrag • Basically, hop counts from a external device • Stages • installation, propagation, adaptation, removal • Adaptation formula
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
Get Location with Gradient Precision proportional to communication radius, affected by node density.
MultiGrad - vFrag - One virtual pFrag emulating multiple pFrags - Save memory space - Any pFrag can issue a request for Gradient
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
Tessellation - Issues • Time issues - settling time, randomness, large moves • Precision • Initial field strength - neither too low nor too high would work
Channel Operator • End-to-End communication • Gradient, Tracers and Halos • Gradient issued at the destination • Gradient - a waste of bandwidth? • Cross-traffic prohibited?
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.