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Goal: Programming Computational Matter

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Title: Goal: Programming Computational Matter


1
GoalProgramming Computational Matter
  • Develop engineering principles and programming
    techniques for directing the behavior of systems
    composed of myriad potentially unreliable and
    inaccurately manufactured parts
  • Exploit such systems for implementing and
    managing sensors and effectors

2
Outcomes
  • Coordinated action from massive assemblies of
    sensors, actuators, and computation
  • Good collective behavior from bad parts
  • Novel materials that enhance precision and
    strength, using computation
  • Self-configuring mechanical systems
  • ?? Biology ??

3
Example
  • signal processing surfaces

4
Example
  • Self-configuring mechanical system

5
Example
  • Something using biology/chemistry?

6
Why now? - The opportunity
  • Ultra-scale silicon
  • molecular electronics
  • biological subtrates for computing
  • kernel technologies to build or grow vast numbers
    of identical units at almost no cost - but we
    dont know how to program them!

7
Characteristics of Computational Matter
  • too many elements to be individually programmed,
    or even to be named.
  • variable interconnect, unknown a priori, possibly
    time varying
  • imperfect elements and interconnect
  • dead or sick on arrival
  • flakey (perhaps gives the wrong result)

8
Characteristics - II
  • may be heterogeneous or homogeneous
  • may include sensors or effectors
  • communication mostly local
  • but there can be wormholes
  • may need to discover the topology

9
Hard Problems
  • how to specify desired global behavior using only
    local interactions
  • how to specify the agents that must act without
    the assignment of names
  • how to enforce acceptable global behavior in the
    face of unreliable parts and interconnect
  • how to elicit prescribed geometric behavior
    using only locally obtained topological
    information

10
Approaches to the hard problems
  • how to specify desired global behavior using only
    local interactions
  • build on organizational metaphors from physics
    that exploit the spatial locality of the real
    world
  • build on organizational metaphors from biology
    that exploit the massive redundancy available in
    the real world

11
Example Growing a complex pattern like a plant
12
Approaches to the hard problems
  • how to specify the agents that must act without
    the assignment of names
  • Use boolean combinations of markers and counters
    to select agents intensionally rather than
    extensionally.
  • Massive numbers of elements make this an
    effective strategy.

13
Approaches to the hard problems
  • how to enforce acceptable global behavior in the
    face of unreliable parts and interconnect
  • allow late binding of
  • interconnect topology
  • available resources
  • build on organizational metaphors from biology
    that exploit the massive redundancy available in
    the real world

14
Approaches to the hard problems
  • how to elicit prescribed geometric behavior
    using only locally obtained topological
    information
  • Clues about the geometry can be extracted using
    constraints that come from the physical embedding
    of the system in the real world.

15
Example
  • The number of computational elements within a
    particular hop-count radius gives an estimate of
    the local scalar curvature
  • picture here

16
Benefitsto be wordsmithed
  • Cheap insanely big computers
  • radically customizable manufacturing
  • cheap, sophisticated, massive sensor applications
  • spectacularly enhanced interfaces to the
    biological and chemical worlds
  • new communities of researchers across computing,
    biology, and chemistry

17
Milestones- to be fixed
  • Note need to build stuff in conventional
    technology, short term goal
  • build interfaces between the different subtrates

18
  • Examples
  • signal-proc,
  • self-configuring mech sys
  • biology example
  • Milestones
  • infrastructure
  • links to application

19
Transition
20
END
  • Stuff after here is extra

21
Hard Problems
  • Programming and design
  • Naming in large dynamic spaces
  • application design
  • Discovering metaphors appropriate to a particular
    substrate
  • specification
  • compilation

22
Hard Problems
  • Imposing reliability
  • Determining the relation between system
    reliability and the error characteristics of the
    components
  • Imposing structure
  • performance predicting and performance debugging
  • infrastructure development

23
Hard Problems
  • Stratification of the design and understanding
    sensitivity to substrate characteristics, how
    changes at one level effect changes at other
    levels

24
Approaches to the hard problems
25
Milestones
  • 3-year goals identifying metaphors for
    particular substrates - tying to properties of
    substrates differentiates this program
  • 5-years behavior of metaphors wrt properties of
    substrates

26
Goal
  • To effectively program huge systems with rapidly
    variable properties.
  • Computational manifolds
  • Systems composed of billions of parts, changing
    dynamically, too many to name, focus on
    communications problem.
  • What happens 20 or 30 years from now when there
    are zillions of nodes. What kind of naming and
    addressing schemes could we use?
  • What infrastructure mechanisms to allow
    programming these very large sets?
  • Naming is one of the problems that need to be
    solved?

27
Goal
  • To develop engineering principles and programming
    techniques for directing the behavior of systems
    composed of myriad potentially unreliable and
    inaccurately manufactured parts
  • To exploit such systems for computing, sensing,
    and control applications

28
Why now? - the Challenge
performance/cost
feature size
29
How can we program amorphous stuff?
  • organisms are composed of myriad cells that
    cooperate to achieve common goals
  • biology provides organizational metaphors for new
    engineering principles
  • examples from developmental biology illustrate
    this point

30
A botanical metaphor
We organize a process in terms of growing
points. They make structures that exhibit
tropisms toward particular chemical
gradients. The growing points may lay down
materials. Materials may secrete pheromones that
attract or repel other growing points. Growing
points may split, die off, or join. Support for
this abstraction may be programmed as a uniform
state machine in each computational particle.
31
Biologically-inspired engineering
  • not biomimetics
  • behavior is correct if it is acceptable
  • multiple representations and redundant means of
    achieving goals
  • defects at one level can be compensated for by
    changes at another level
  • continuity of representations

32
A new interdisciplinary community
Biology chemical engineering MEMS computation cont
rol
33
Engineering-inspired biology
  • Interfacing to the chemical world
  • Molecular-scale manufacturing
  • Microbial robotics
  • Minimal organisms

34
Computation is free
  • biological systems employ massive redundant
    computation
  • wasteful computation can be used to decrease
    the need for
  • strength of materials
  • precision of manufacture
  • reliability of communication

35
Spectrum of specification
  • degrees of specification
  • of outcome
  • of design
  • of manufacturing process

36
Diversity and redundancy
  • in representations
  • in methods
  • in goals

37
Assembly of information-rich molecular-scale
systems
38
Some stuff thats missing
  • Militarily relevant applications
  • exciting applications
  • compelling example
  • specific challenges
  • metrics
  • what it takes to do it
  • technical approaches
  • expected outcome

39
Amorphous computing is
  • Not emergent behavior
  • Not pretty pictures
  • It is engineering of mechanisms with
    prespecified, well-defined behavior

40
Robust systems
  • Fault-tolerance can be emergent
  • but can also be a result of designed behavior
  • choice of levels for modularity and redundancy
  • continuity of representations
  • unary is better than binary
  • this is the crucial feature of analog
    representations
  • noise may be OK
  • link to sensors and effectors

41
A scientific and technological effort to identify
  • methods for obtaining coherent behavior from the
    cooperation of large numbers of unreliable parts
    that are interconnected in unknown, irregular,
    and time-varying ways
  • techniques for instructing myriad programmable
    entities to cooperate to achieve particular goals
  • engineering principles and languages that can be
    used to observe, control, organize, and exploit
    the behavior of programmable multitudes

42
  • Not everything changes at the same time scale
  • some things change slowly

43
Another goal
  • Applications of active materials to sensors.
  • Specific example sensor arrays.

44
Goal
  • The engineering basis for designing effective
    organizations for a given computational
    substrate.
  • Variability, defect rate, all inputs to that.

45
  • 3-year goals identifying metaphors for
    particular substrates - tying to properties of
    subtrates differentiates this program
  • 5-years behavior of metaphors wrt properties of
    substrates
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