Paintable Computer

Ting YanCS 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

Hardware Platform

Memory Organization

Self-contained Executables

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 1min1 HCHCt

N

HCHCD

t

N

ii

t

11

1

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

Gradient Adaptation

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 - Details

Tessellation - Issues

• Time issues - settling time, randomness, large moves

• Precision

• Initial field strength - neither too low nor too high would work

Tessellation Adaptation

Channel Operator

Channel Operator

• End-to-End communication

• Gradient, Tracers and Halos

• Gradient issued at the destination

• Gradient - a waste of bandwidth?

• Cross-traffic prohibited?

Coordination Operator

Coordination - Example

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.

Diffusion - Result