isat rpm 7/14 2003-6 goldsteinlee1 seth copen goldstein 7/14/06 thoughts on programming programmable...

ISAT RPM 7/14 © 2003-6 Goldstein&Lee 1

Seth Copen Goldstein

7/14/06

Thoughtson

Programming Programmable Matter

This set of slides is meant to get us started. I don’t assume that it contains only answers,

only the start on a description of the problem.


Imagine• A conductive

material…• …with

intelligence, actuation, and sensing…

• …that can morph into shapes under software control…

• …in reaction to external stimuli






• …in reaction to external stimuli 5mm

8cm






• …in reaction to external stimuli

4cm


Programmable MatterEnsemble of programmable, mobile units which

can form dynamic 3D physical objects.

Stoy, 03

ISAT, 05

Stoddart/UCLA’06

Lipson, 05


Field Programmable Factory• Programmable Matter “Duct Tape”• Three kinds of materials

– Display only - sandtable– Moderate adhesion forces - antenna– Bullet proof – shovel, bullet-proof humvee door

• Doors on demand– PM downloads a mold– Add dirt & Elastomeric x-linked polymer– Results in permanent bullet proof objects

• Other uses: E.g., One-handed smart casts, bandages, …


Other DoD Applications•Sand table•Smart (one-handed) bandages•Frequency selective surfaces•Programmable Rucksack•3D Fax•“PJ” equipment

•Metrics & Time Frame– Near term: slow, low adhesion forces, plugged in– Medium term: slow, plugged in– Far term: realtime, mobile


Requirements• Create Human scale artifacts

Massive numbers of units• Capture fine details of object

Micron-scale units• Thus each unit must be:

– Inexpensive – Robust– Capable of

•Running an independent program•Communicating with others•Locomotion•Adhesion


Dipole with tuning stubs:Tuned to 8GHz, 800MHz bandwidth

1cm

4cm

Dipole with tuning stubs:Tuned to 8GHz, 800MHz bandwidth

1cm

4cm

•Ensemble of elements which each contain–Processor–Communication–Power–Sensing–Display–Actuation–Programmable adhesive

•Can be programmed to form dynamic shapes.

Programmable Matter

5mm

8cm

Dipoletuned to 4GHz,200MHzbandwidth

5mm

8cm


Will be a video


• –Processor–Communication–Power–Sensing–Display–Actuation–Programmable adhesive

•Can be programmed to form dynamic shapes.

•Key Questions:–Is it really possible?–Why do it?–Can we program it?

Programmable Matter•Ensemble of elements which

5mm

8cm


5mm

8cm



What would it take to make this real?

The main conclusions of our study:1. Building such a material is feasible2. The time to achieve it can be shortened by

focused investment3. Protennas are an important mid-term

military reason to do this4. Furthermore, there are other important

military applications• Some mid-term and some longer term

5. Major DARPA-hard technical challenges in 3D manufacturing and in massive-scale programming, if overcome, are likely to have significant spinoff effects


Hardware* Summary

*: Not addressing the programming of the ensemble

Actuators

Electronics

Assembly

Adhesion

Power

Applications

Today 2 5 10+Demo at

1mmSA by

Chemistry

Demo Electrostati

c“Click”

Surface forces

In simulatio

n

Demo Mobile


If we can build it …2 years x years

Structural Strength low highMaterial Properties conducting ?Reconfig Speed slow fastControlMobility fixed mobileGranularity mm m

*: Not addressing the programming of the ensemble


What about the software?• Is Programming PM just another

parallel programming problem?• What are the characteristics of the

problem space?– Machine– Application area– Environment

• What programming model should we choose?

• How do we implement the model?


Parallel Machines

Big IronEmbedded

?

Private Network

SMMP MPP

Shared Network

NoW GridDist

Systems

Sensor Nets

Prog Matter

Modular Robots


Features of Importance*

• Scaling (# of PEs)• PE Compute Power• Fault Rate• Levels of communication hierarchy• Communication reach• Interconnect topology• Resource limitations (energy/heat)• Control over the environment• Mobility• (Global) Clock• Beacons

*: To the programmer


Scaling (# of PEs)• SMMPs: 16-128• MPPs: ~1024• Grid: ?• Sensor Nets: thousands?• PM: Millions

SMMP MPP NoW Grid DS SN PM


PE Capabilities• Only in SN and PM does is the programmer

aware of the limitations• PM spans many possibilies:

– Silicon/MEMS: ~386 class processor– Synthetic Bio-based: ~8080 or less– DNA-tiles: ~ten-state FA



Fault Rate• Traditional Parallel programming assumes perfect

machines and perfect networks• Distributed systems assume low fault rates, but

programmer must support retry/timeout• SN/PM: node failures/comm failures/incorrect comm

SMMP MPP NoW Grid DS SN PMNone

Some

Fact of life


Levels of Communication Hierarchy• Big Iron has two levels of hierarcy: local/remote• SN?• PM: local/neighborhood/remote

SMMP MPP NoW Grid DS SN PM2

3


Communication Reach• Big Iron assumes can coordinate globally

(due to scale of machine and 2-level hierarchy)• SN assumes global coordinatation possible (but

costly)• PM prohibits global coordination


Entire system

Local neighborhood


Interconnect Topology• Modern big-iron programs are topology agnostic

(it was just too hard to tailor algorithms to the topoloigy and the 2-level hierarchy made it not important)

• DistSystems: network is a cloud• SN: topology important, but semi-static• PM: topology important AND CHANGING

SMMP MPP NoW Grid DS SN PMNonefixed

changing


Resource Awareness• Big-iron: only resource is compute nodes• SN: energy awareness is vital• PM: (less?) energy awareness needed (but need to

track heat dissipation?)



Mobility• Big-iron• SN: little• PM: Raison d’etre



Control over/Reactivity to environment

• Big-iron• SN: very little• PM: Possible to change shape in response to

environment



Adding it all up• PM appears not just harder, but entirely

different!• Different (and harder?)

– Larger scale systems– Using less capable processors– With higher fault rates– In a 3-tiered communication hierarchy– With only local reach– In a changing topology– And limited resources– Creating mobile ensembles


The Environment• PM must deal with uncertainty

SMMP MPP NoW Grid DS SN PMControlled

Uncontrolled


Application Area• Main application of PM (at least for

now) is rendering!– Does not require precise deterministic

reproducible result– Has real-time aspects– (What about modular robotic applications?)

• Contrast with uses for big-iron:– E.g., Bank-accounts– Deterministic, Reproducible


Application Area (saving grace?)

• Significant success in “rendering”– Stoy– Nagpal– DeRosa– Klavins– (others please)

• Can be stochastic(Given machine characteristics, may be required. We should embrace this, not shy away from it)


Where have we been?• Insipiration and related work: (partial and

unordered)• Graph grammers [Klavins]• Origami shape language [Ragpal]• Economic based [Miller]• Emergent behvaior [SFI et. Al.]• Teramac [Kuekes]• Paintable Computing [Butera]• Sensor Nets [Culler, Madden, …]• Information-Based Complexity [Traub]• Programming Work [Koditschek]• Topological Approach [Ghrist]


Possible Models• Hierarchical

– Composing Funnels [Koditschek]– Reactive Systems [statecharts?]– Actors [agha]

• Emergent– Achieve global behavior via emergence

from local rules– Gradient Methods– Graph Grammers– Economic based

• Do these work? Is there a better one?• Is Life (aka DNA) example of

hierarchical emergent system?


Implementing the Model• MIMD/SIMD not practical• SPMD is probably the only possible

approach– One logical source for the ensemble– (What about Butera?)


Programming Work• Use inspiration of programming

work in the context of robotic controllers

• “Funnels” support fault tolerance and program composition

),( uxfx E

Lyapunov Function:

y)(yg

Dynamical “Symbol,”

Goal Set: G

)(ygy

Vector Field: g

Obstacle Set: O

Dynamical “Behavior,”

(model)(instance)

)(xfx

)(yhx


Pwork Vs. Pcomp• Pwork takes into account time to

accomplish a task (but not just Real time)

• Pwork:– Uncertainty in environment– Energy cost of computing– Dynamics of real world

• Pcomp: Isn’t this just pwork with a good abstraction?


Pcomp• Reality of Pcomp is pushing e-

• We ignore the e- because:– There are many of them!

• Abstraction: The bit• Algebra: Boolean logic• Gain: Transistor

• (Note: sub-45nm Pcomp may be just like Pwork!)


How to make pwork pcomp• Abstraction: Energy gradient• Algebra: Topology-based?• Gain: Funnels

• Composing funnels (at least sequentially) is “tractable”

• Algebra of funnels may not be turing-complete

• Where Cost?


Another Approach: IBC• Information-based complexity

[Traub88]• Basis of IBC: Information

– Has cost– Is tainted– Is partial

• Used to study complexity of continuous functions, error

• Key result: randomness is essential


10-3m

Milli-

10-2m

What is “nano”

10m 1m 10-1m

http://www.powersof10.com/

10-7m10-6m

Micro-

10-5mlymphocyte

10-4m10-8m

10-9m

Nano-


Focusing DARPA nanotech research•All definitions deal with size:

– building electronic circuits and devices from single atoms and molecules

– things smaller than 100 nanometers– the molecular level, atom by atom– control of matter on the nanometer length

scale•What if want to use nanotech to make a

jeep? A shovel? An antenna?Nanotechnology: The science and technology

of manipulating massive numbers of nanoscale components


Synergy: CS + Nano• Computer science:

The science of controlling complexitythrough abstraction.

• Nanotechnology:Technology for constructing andmanipulating billions of nanoscale

items.• For example, Manage:

– Randomness/regularity of bottom-up assembly

– Build in defect-tolerance– Complexity of manufacturing


Conclusions• There is a DoD leap ahead capability

– Improved antennas: lower power, better performance, more flexible

– Field programmable factory:reduce load, increase flexibility, improved logisitics, new capabilities

• Basic technology available for creating robust PM artifacts

• Understanding of challenges in manufacturing individual elements

• Today’s focus: understand software

isat rpm 7/14 2003-6 goldsteinlee1 seth copen goldstein 7/14/06 thoughts on programming programmable...

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