EEL 5930 sec. 5, Spring 05 Physical Limits of Computing

1
EEL 5930 sec. 5, Spring 05Physical Limits of
Computing
http//www.eng.fsu.edu/mpf

• Slides for a course taught byMichael P. Frankin
  the Department of Electrical Computer
  Engineering

2
Module 9 Fundamental Physical Limits of
Computing

• A Brief Survey

3
Fundamental Physical Limits of Computing
ImpliedUniversal Facts
Affected Quantities in Information Processing
Thoroughly ConfirmedPhysical Theories
Speed-of-LightLimit
Communications Latency
Theory ofRelativity
Information Capacity
UncertaintyPrinciple
Information Bandwidth
Definitionof Energy
Memory Access Times
QuantumTheory
Reversibility
2nd Law ofThermodynamics
Processing Rate
Adiabatic Theorem
Energy Loss per Operation
Gravity
4
A Slightly More Detailed View
5
The Speed-of-Light Limit on Information
Propagation Velocity

• What are its implications for future computer
  architectures?

6
Implications for Computing

• Minimum communications latency!
• Minimum memory-access latency!
• Need for Processing-in-Memory architectures!
• Mesh-type topologies are optimally scalable!
• Hillis, Vitanyi, Bilardi Preparata
• Together w. 3-dimensionality of space implies
• No network topology with ?(n3) connectivity (
  nodes reachable in n hops) is scalable!
• Meshes w. 2-3 dimensions are optimally scalable.
• Precise number depends on reversible computing
  theory!

7
How Bad it Is, Already

• Consider a 3.2 GHz processor (off todays shelf)
• In 1 cycle a signal can propagate at most
• c/(3.2 GHz) 9.4 cm
• For a 1-cycle round-trip to cache memory back
• Cache location can be at most 4.7 cm away!
• Electrical signals travel at 0.5 c in typical
  materials
• In practice, a 1-cycle memory can be at most 2.34
  cm away!
• Already ? logics in labs at 100 GHz speeds!
• E.g., superconducting logic technology RSFQ
• 1-cycle round trips only within 1.5 mm!
• Much smaller than a typical chip diameter!
• As f?, architectures must be increasingly local.

8
Latency Scaling w. Memory Size

• Avg. time to randomly access anyone of n bits of
  storage (accessibleinformation) scales as
  ?(n1/3).
• This will remain true in all future technologies!
• Quantum mechanics gives a minimum size for bits
• Esp. assuming temperature pressure are limited.
• Thus n bits require a ?(n)-volume region of
  space.
• Minimum diameter of this region is ?(n1/3).
• At lightspeed, random access takes ?(n1/3) time!
• Assuming a non-negative curvature region of
  spacetime.
• Of course, specific memory technologies (or a
  suite of available technologies) may scale even
  worse than this!

?(n1/3)
n bits
9
Scalability Maximal Scalability

• A multiprocessor architecture accompanying
  performance model is scalable if
• it can be scaled up to arbitrarily large
  problem sizes, and/or arbitrarily large numbers
  of processors, without the predictions of the
  performance model breaking down.
• An architecture ( model) is maximally scalable
  for a given problem if
• it is scalable, and if no other scalable
  architecture can claim asymptotically superior
  performance on that problem
• It is universally maximally scalable (UMS) if it
  is maximally scalable on all problems!
• I will briefly mention some characteristics of
  architectures that are universally maximally
  scalable

10
Shared Memory isnt Scalable

• Any implementation of shared memory requires
  communication between nodes.
• As the of nodes increases, we get
• Extra contention for any shared BW
• Increased latency (inevitably).
• Can hide communication delays to a limited
  extent, by latency hiding
• Find other work to do during the latency delay
  slot.
• But, the amount of other work available is
  limited by node storage capacity, parallizability
  of the set of running applications, etc.

11
Unit-Time Message Passing Isnt Scalable

• Model Any node can pass a message to any other
  in a single constant-time interval (independent
  of the total number of nodes)
• Same scaling problems as shared memory!
• Even if we assume BW contention (traffic) isnt a
  problem, unit-time assumption is still a problem.
• Not possible for all N, given speed-of-light
  limit!
• Need cube root of N asymptotic time, at minimum.

12
Many Interconnect Topologies arent Scalable

• Suppose we dont require a node can talk to any
  other in 1 time unit, but only to selected
  others.
• Some such schemes still have scalability
  problems, e.g.
• Hypercubes
• Binary trees, fat trees
• Butterfly networks
• Any topology in which the number of unit-time
  hops to reach any one of N nodes is of order less
  than N1/3 is necessarily doomed to failure!
• Caveat Except in negative-curvature spacetimes!

13
Only Meshes (or subgraphs thereof) Are Scalable

• See papers by Hillis, Vitanyi, Bilardi
  Preparata
• 1-D meshes
• linear chain, ring, star (w. fixed of arms)
• 2-D meshes
• square grid, hex grid, cylinder, 2-sphere,
  2-torus,
• 3-D meshes
• crystal-like lattices w. various symmetries
• Caveat
• Scalability in 3rd dimension is limited by
  energy/information I/O considerations!

Amorphousarrangementsin ?3d, w. localcomms.,
are also ok
14
Ideally Scalable Architectures
Claim A 2- or 3-D mesh multiprocessor with a
fixed-size memory hierarchy per node is an
optimal scalable computer systems design (for any
application).
Processing Node
Processing Node
Processing Node
Local memory hierarchy(optimal fixed size)
Local memory hierarchy(optimal fixed size)
Local memory hierarchy(optimal fixed size)
Processing Node
Processing Node
Processing Node
Local memory hierarchy(optimal fixed size)
Local memory hierarchy(optimal fixed size)
Local memory hierarchy(optimal fixed size)
Mesh interconnection network
15
Landauers Principle

• Low-level physics is reversible
• Means, the time-evolution of a state is bijective
• Deterministic looking backwards in time
• as well as forwards
• Physical information (like energy) is conserved
• Cannot be created or destroyed, only reversibly
  rearranged and modified
• Implies the 2nd Law of Thermodynamics
• Entropy (unknown info.) in a closed, unmeasured
  system can only increase (as we lose track of the
  state)
• Irreversible bit erasure really just moves the
  bit into surroundings, increasing entropy heat

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Scaling in 3rd Dimension?

• Computing based on ordinary irreversible bit
  operations only scales in 3d up to a point.
• Discarded information associated energy must be
  removed thru surface. Energy flux limited.
• Even a single layer of circuitry in a
  high-performance CPU can barely be kept cool
  today!
• Computing with reversible, adiabatic operations
  does better
• Scales in 3d, up to a point
• Then with square root of further increases in
  thickness, up to a point. (Scales in 2.5
  dimensions!)
• Scales to much larger thickness than irreversible!

17
Universal Maximum Scalability

• Existence proof for universally maximally
  scalable (UMS) architectures
• Physics itself is a universal maximally scalable
  architecture because any real computer is
  merely a special case of a physical system.
• Obviously, no restricted class of real computers
  can beat the performance scalability of physical
  systems in general.
• Unfortunately, physics doesnt give us a very
  simple or convenient programming model.
• Comprehensive expertise at programming physics
  means mastery of all physical engineering
  disciplines chemical, electrical, mechanical,
  optical, etc.
• Wed like an easier programming model than this!

18
Simpler UMS Architectures

• (I propose) any practical UMS architecture will
  have the following features
• Processing elements characterized by constant
  parameters (independent of of processors)
• Makes it easy to scale multiprocessors to large
  capacities.
• Mesh-type message-passing interconnection
  network, arbitrarily scalable in 2 dimensions
• w. limited scalability in 3rd dimension.
• Processing elements that can be operated in an
  arbitrarily reversible way, at least, up to a
  point.
• Enables improved 3-d scalability in a limited
  regime
• (In long term) Have capability for
  quantum-coherent operation, for extra perf. on
  some probs.

19
Limits on Amount of Information Content
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Some Quantities of Interest

• We would like to know if there are limits on
• Information density
• Bits per unit volume
• Affects physical size and thus propagation
  delayacross memories and processors. Also
  affects cost.
• Information flux
• Bits per unit area per unit time
• Affects cross-sectional bandwidth, data I/O
  rates, rates of standard-information input
  effective-entropy removal
• Rate of computation
• Number of distinguishable-state changes per
  unit time
• Affects rate of information processing achievable
  in individual devices

21
Bit Density No classical limit

• In classical (continuum) physics, even a single
  particle has a real-valued positionmomentum
• All such states are considered physically
  distinct
• Each position momentum coordinate in general
  requires an infinite string of digits to specify
• x 4.592181291845019587661625618991009 meters
• p 2.393492301938881726153514427394001 kg m/s
• Even the smallest system contains an infinite
  amount of information! ? No limit to bit
  density.
• This picture is the basis for various analog
  computing models studied by some theoreticians.
• Wee problem Classical physics is dead wrong!

22
The Quantum Continuum

• In QM, still ? uncountably many describable
  states (mathematically possible wavefunctions)
• Can theoretically take infinite info. to describe
• But, not all this info has physical relevance!
• States are only physically distinguishable when
  their state vectors are orthogonal.
• States that are only indistinguishably different
  can only lead to indistinguishably different
  consequences (resulting states)
• due to linearity of quantum physics
• There is no physical consequence from presuming
  an infinite of bits in ones wavefunction!

23
Quantum Particle-in-a-Box

• Uncountably manycontinuouswavefunctions?
• No, can expresswave as a vectorover
  countablymany orthogonalnormal modes.
• Fourier transform
• High-frequencymodes have higherenergy (Ehf)
  alimit on average energy impliesthey have low
  probability.

24
Ways of Counting States

• The entire field of quantum statistical mechanics
  is all about this, but here are some simple ways
• For a system w. a constant of particles
• of states numerical volume of the
  position-momentum configuration space (phase
  space)
• When measured in units where h1.
• Exactly approached in the macroscopic limit.
• Unfortunately, of particles is not usually
  constant!
• Quantum field theory bounds
• Smith-Lloyd bound. Still ignores gravity.
• General relativistic bounds
• Bekenstein bound, holographic bound.

25
Smith-Lloyd Bound
Smith 95Lloyd 00

• Based on counting modes of quantum fields.
• S entropy, M mass, V volume
• q number of distinct particle types
• Lloyds bound is tighter by a factor of
• Note
• Maximum entropy density scales with only the 3/4
  power of mass-energy density!
• E.g., Increasing entropy density by a factor of
  1,000 requires increasing energy density by
  10,000.

26
Whence this scaling relation?

• Note that in the field theory limit, S ? E3/4.
• Where does the ¾ power come from?
• Consider a typical mode in field spectrum
• Note that the minimum size of agiven wavelet is
  its wavelength ?.
• of distinguishable wave-packet location states
  in a given volume ? 1/?3
• Each such state carries just a little entropy
• occupation number of that state ( of photons in
  it)
• ?1/?3 particles each energy ?1/?, ?1/?4 energy
• S?1/?3 ? E?1/?4 ? S?E3/4

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Whence the distribution?

• Could the use of more particles (with less energy
  per particle) yield greater entropy?
• What frequency spectrum (power level or particle
  number density as a function of frequency) gives
  the largest states?
• Note ? a minimum particle energy in finite-sized
  box
• No. The Smith-Lloyd bound is based on the
  blackbody radiation spectrum.
• We know this spectrum has the maximum info.
  content among abstract states, b/c its the
  equilibrium state!
• Empirically verified in hot ovens, etc.

28
Examples w. Smith-Lloyd Bound

• For systems at the density of water (1 g/cm3),
  composed only of photons
• Smiths example 1 m3 box holds 61034 bits
• 60 kb/Å3
• Lloyds example 1 liter ultimate laptop,
  21031 b
• 21 kb/Å3
• Pretty high, but whats wrong with this picture?
• Example requires very high temperaturepressure!
• Temperature around 1/2 billion Kelvins!!
• Photonic pressure on the order of 1016 psi!!
• Like a miniature piece of the big bang. -Lloyd
• Probably not feasible to implement any time soon!

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More Normal Temperatures

• Lets pick a more reasonable temperature 1356 K
  (melting point of copper)
• The entropy density of light is only 0.74
  bits/?m3!
• Less than the bit density in a DRAM today!
• Bit size is comparable to avg. wavelength of
  optical-frequency light emitted by melting copper
• Lesson Photons are not a viable nanoscale info.
  storage medium at ordinary temperatures.
• They simply arent dense enough!
• CPUs that do logic with optical photons cant
  have their logic devices packed very densely.

30
Entropy Density of Solids

• Can easily calculate from standard empirical
  thermochemical data.
• E.g. see CRC Handbook of Chemistry Physics.
• Obtain entropy by integrating heat capacity
  temperature, as temperature increases
• Example result, for copper
• Has one of the highest entropy densities among
  pure elements, at atmospheric pressure.
• _at_ room temperature 6 bits/atom, 0.5 b/Å3
• At boiling point 1.5 b/Å3
• Cesium has one of the highest bits/atom at room
  temperature, about 15 bits/atom.
• But, only 0.13 b/Å3
• Lithium has a high bits/mass, 0.7 bits/amu.

1012denser thanits light!
Related toconductivity?
31
General-Relativistic Bounds

• Note the Smith-Lloyd bound does not take into
  account the effects of general relativity.
• Earlier bound from Bekenstein Derives a limit on
  entropy from black-hole physics
• S lt (2?ER / ?c) nats
• E total energy of system
• R radius of the system (min sphere)
• Limit only attained by black holes!
• Black holes have 1/4 nat entropy per square
  Planck length of surface (event horizon) area.
• Absolute minimum size of a nat 2 Planck lengths,
  square

41039 b/Å3average ent. dens.of a 1 m
radiusblack hole!(Mass?Saturn)
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The Holographic Bound

• Based on Bekenstein black-hole bound.
• The information content I within any surface of
  area A (independent of its energy content!)
  is I A/(2?P)2 nats
• ?P is the Planck length (see lecture on units)
• Implies that any 3D object (of any size) is
  completely definable via a flat (2D) hologram
  on its surface having Planck-scale resolution.
• This information is all entropy only in the case
  of a black hole with event horizonthat surface.

33
Holographic Bound Example

• The age of the universe is 13.7 Gyr 1 WMAP.
• Radius of currently-observed part would thus be
  13.7 Glyr
• But, due to expansion, its edge is actually 46.6
  Glyr away today.
• Cosmic horizon due to acceleration is 62 Glyr
  away
• The universe is flat, so Euclidean formulas
  apply
• The surface area of the eventually-observable
  universe is
• A 4pr2 4p(62 Glyr)2 4.331054 m2
• The volume of the eventually-observable universe
  is
• V (4/3)pr3 (4/3)p(62 Glyr)3 8.481080 m3
• Now, we can calculate the universes total info.
  content, and its average information density!
• I An/4?P2 (pr2/?P2) n 4.1510123 n
  5.9810123 b
• I/V 7.061042 b/m3 7.0610-3 b/fm3 1b/(.19
  fm)3
• A proton is 1 fm in radius.
• Very close to 1 bit per quark-sized volume!

34
Do Black Holes Destroy Information?

• Currently, it seems that no one completely
  understands exactly how information is preserved
  during black hole accretion, for later
  re-emission in the Hawking radiation.
• Perhaps via infinite time dilation at event
  horizon?
• Some researchers have claimed that black holes
  must be doing something irreversible in their
  interior (destroying information).
• However, the arguments for this may not be valid.
• Recent string theory calculations contradict this
  claim.
• The issue seems not yet fully resolved, but I
  have many references on it if youre interested.
• Interesting note Stephen Hawking recently
  conceded a bet he had made, and decided black
  holes do not destroy information.

35
Implications of InformationDensity Limits

• There is a minimum size for a bit-device.
• thus there is a minimum communication latency to
  randomly access a memory containing n bits
• as we discussed earlier.
• There is also a minimum cost per bit, if there is
  a minimum cost per unit of matter/energy.
• Implications for communications bandwidth limits
• coming up

36
Some Quantities of Interest

• We would like to know if there are limits on
• Information density
• Bits per unit volume
• Affects physical size and thus propagation
  delayacross memories and processors. Also
  affects cost.
• Information flux
• Bits per unit area per unit time
• Affects cross-sectional bandwidth, data I/O
  rates, rates of standard-information input
  effective entropy removal
• Rate of computation
• Number of distinguishable-state changes per
  unit time
• Affects rate of information processing achievable
  in individual devices

37
Communication Limits

• Latency (propagation-time delay) limit from
  earlier, due to speed of light.
• Teaches us scalable interconnection technologies
• Bandwidth (information rate) limits
• Classical information-theory limit (Shannon)
• Limit, per-channel, given signal bandwidth SNR.
• Limits based on field theory (Smith/Lloyd)
• Limit given only area and power.
• Applies to I/O, cross-sectional bandwidths in
  parallel machines, and entropy removal rates.

38
Hartley-Shannon Law

• The maximum information rate (capacity) of a
  single wave-based communication channel is C
  B log (1S/N)
• Where
• B bandwidth of channel, in frequency (1/Tper)
  units
• S signal power level
• N noise power level
• The log base gives the information unit, as usual
• Law not sufficiently powerful for our purposes!
• Does not tell us how many effective channels are
  possible,
• given available power and/or area.
• Does not give us any limit if..
• we are allowed to indefinitely increase bandwidth
  used,
• or indefinitely decrease the noise floor (better
  isolation).

39
Density Flux

• Note that any time you have
• a limit ? on density (per volume) of something,
• a limit v on its propagation velocity,
• this automatically implies
• a limit F ?v on the flux
• by which I mean amount per time per area
• Note also we always have a limit (c) on velocity!
• At speeds near c, must account for relativistic
  effects
• Often, slower velocities vltc may also be
  relevant
• Electron saturation velocity, in various
  materials
• Max velocity of air or liquid coolant in a
  cooling system
• Thus, a density limit ? implies flux limit F?c

Cross-section
v
40
Relativistic Effects

• For normal matter (bound massive-particle states)
  moving at a velocity v approaching c
• Entropy density increases by a factor 1/?
• Due to relativistic length contraction
• But, energy density increases by factor 1/?2
• Both length contraction mass amplification!
• ? entropy density scales up only w. square root
  (1/2 power) of energy density from high velocity
• Note that light travels at c already,
• its entropy density scales with energy density
  to the 3/4 power. ? Light wins in limit as v?c.
• If you want to maximize entropy flux/energy flux

41
Max. Entropy Flux Using Light
Smith 95

• Where
• FS entropy flux
• FE energy flux
• ?SB Stefan-Boltzmann constant, ?2kB4/60c2?3
• This is derived from the same field-theory
  arguments as the information density bound.
• Again, the blackbody spectrum maximizes the
  entropy flux, given the energy flux
• Because it is the equilibrium spectrum!

42
Entropy Flux Examples

• Consider a 10 cm wide, flat, square wireless
  tablet with a 10 W power supply.
• Whats its maximum possible rate of bit
  transmission?
• Independent of spectrum used, noise floor, etc.
• Answer
• Energy flux 10 W/2(10 cm)2 (use both sides)
• Smiths formula gives 2.21021 bps
• Whats the rate per square nanometer surface?
• Only 109 kbps! (ISDN speed, in a 100 GHz CPU?)
• 100 Gbps/nm2 ? nearly 1 GW power!

Light is not informationally dense enough for
high-bandwidth communication between densely
packed nanometer-scale devices at reasonable
power levels!!!
43
Entropy Flux w. Atomic Matter

• Consider liquid copper (?S 1.5 b/Å3) moving
  along at a leisurely v 10 cm/s
• BW 1.5x1027 bps through the 10-cm wide square!
• A million times higher BW than with 10W light!
• 150 Gbps/nm2 entropy flux!
• Plenty for nano-scale devices to talk to their
  neighbors
• Most of this entropy is in the conduction
  electrons...
• Less conductive materials have much less entropy
• Can probably do similarly well (or better) just
  moving the electrons in solid copper. (Higher
  velocities attainable.)
• Nano-wires can probably carry gt100 Gbps
  electrically.
• Lesson
• For maximum bandwidth density at realistic power
  levels, encode information using states of matter
  (electrons) rather than states of radiation
  (light).

Exercise Kinetic energy flux?
44
Some Quantities of Interest

• We would like to know if there are limits on
• Infropy density
• Bits per unit volume
• Affects physical size and thus propagation
  delayacross memories and processors. Also
  affects cost.
• Infropy flux
• Bits per unit area per unit time
• Affects cross-sectional bandwidth, data I/O
  rates, rates of standard-information input
  effective entropy removal
• Rate of computation
• Number of distinguishable-state changes per
  unit time
• Affects rate of information processing achievable
  in individual devices

45
Subsection II.B.5.hEnergy Cost of Information
Erasure

• This should probably be moved to a later module
  on limits

46
Von Neumann / Landauer (VNL) bound for bit
erasure

• The von Neumann-Landauer (VNL) lower bound for
  energy dissipation from bit erasure
• First alluded to by John von Neumann in a 1949
  lecture
• Developed more explicitly by Rolf Landauer (IBM)
  in 1961.
• Oblivious erasure/overwriting/forgetting of a
  known logical bit really just moves the
  information that the bit previously contained to
  the environment
• We lose track of that information and so it
  becomes entropy.
• Leads to fundamental limit of kT ln 2 for
  oblivious erasure.
• This particular limit could only possibly be
  avoidable through reversible computing.
• Reversible computing de-computes unwanted bits,
  rather than obliviously erasing them!
• This can avoid entropy generation, enabling the
  signal energy to be preserved for later re-use,
  rather than being dissipated.

47
Illustration of VNL Principle

• Either of 2 digital states is initially encoded
  by any of N possible physical microstates
• Illustrated as 4 in this simple example (the real
  number would usually be much larger)
• Initial entropy (given the digital state) S
  Logmicrostates Log 4 2 bits.
• Now, suppose some mechanism resets the digital
  state to 0 regardless of what it was before.
• Reversibility of physics ensures this bit
  erasure operation cant possibly merge two
  microstates, so it must double the number of
  possible microstates in the digital state!
• Entropy S Logmicrostates increases by Log 2
  1 bit (Log e)(ln 2) kB ln 2.
• To prevent entropy from accumulating locally, it
  must be expelled into the environment.

Microstates representinglogical 0
Microstates representinglogical 1
Entropy S log 4 2 bits
Entropy S' log 8 3 bits
Entropy S log 4 2 bits
?S S' - S 3 bits - 2 bits 1 bit
48
Reversible Computing

• A reversible digital logic operation is
• Any operation that performs an invertible
  (one-to-one) transformation of the devices local
  digital state space.
• Or at least, of that subset of states that are
  actually used in a design.
• Landauers principle only limits the energy
  dissipation of ordinary irreversible
  (many-to-one) logic operations.
• Reversible logic operations could dissipate much
  less energy,
• Since they can be implemented in a
  thermodynamically reversible way.
• In 1973, Charles Bennett (IBM Research) showed
  how any desired computation can in fact be
  performed using only reversible logic operations
  (with essentially no bit erasure).
• This opened up the possibility of a vastly more
  energy-efficient alternative paradigm for digital
  computation.
• After 30 years of (sporadic) research, this idea
  is finally approaching the realm of practical
  implementability
• Making it happen is the goal of the RevComp
  project.

49
How Reversible Logic Avoids the von
Neumann-Landauer Bound

• We arrange our logical manipulations to never
  attempt to merge two distinct digital states,
• but only to reversiblytransform them fromone
  state to another!
• E.g., illustrated is a reversible
  operationcCLR (controlled clear)
• Non-oblivious erasure
• It and its inverse (cSET)enable arbitrary logic!

a blogic 00
logic 01
a0a1
logic 10
logic 11
b0 b1
50
Information erasure

• Suppose we have access to a subsystem containing
  one bit of information (which may or may not be
  entropy).
• Suppose we now want to erase that bit
• I.e., restore it unconditionally to a standard
  state, e.g. 0
• So we can later compute some new information in
  that location.
• But the information/entropy in that bit
  physically cannot just be irreversibly destroyed.
• We can only ever do physically reversible
  actions, e.g.,
• Move/swap the information out of the bit
• Store it elsewhere, or let it dissipate away
• If you lose track of the information, it becomes
  entropy!
• If it wasnt already entropy. ? Important to
  remember!
• Or, reversibly transform the bit to the desired
  value
• Requires uncomputing the old value
• based on other knowledge redundant with that old
  value

51
Energy Cost of Info. Erasure

• Suppose you wish to erase (get rid of) 1 bit of
  unwanted (garbage) information by disposing of
  it in an external system at temperature T.
• T 300 K terrestially, 2.73 K in cosmic µwave
  background
• Adding that much information to the external
  system will require adding at least E bT energy
  to your garbage dump. (Where b 1 bit log
  2.)
• Note that this is true by the very definition of
  temperature!
• Expressed in natural log units, this is E kBT
  ln 2 energy.
• _at_ room temperature 18 meV _at_ 2.73 K 0.16 meV.
• Landauer_at_IBM (1961) first proved this relation
  between bit-erasure and energy.
• Though a similar claim was made by von Neumann in
  1949.

52
Landauers 1961 Principle from basic quantum
theory
Before bit erasure
After bit erasure
Ndistinctstates



sN-1
s?N-1
0
0
2Ndistinctstates
Unitary(1-1)evolution
s'0
s?N
1
0
Ndistinctstates




s'N-1
s?2N-1
1
0
Increase in entropy S log 2 k ln 2. Energy
lost to heat ST kT ln 2
53
Bistable Potential-Energy Wells

• Consider any system having an adjustable,
  bistable potential energy surface (PES) or well
  in its configuration space.
• The two stable states form a natural bit.
• One state represents 0, the other 1.
• Consider now the P.E. well havingtwo adjustable
  parameters
• (1) Height of the potential energy
  barrierrelative to the well bottom
• (2) Relative height of the left and rightstates
  in the well (bias)

(Landauer 61)
0
1
54
Possible Parameter Settings

• We will distinguish six qualitatively different
  settings of the wells parameters, as follows

BarrierHeight
Direction of Bias Force
55
One Mechanical Implementation
Stateknob
Rightwardbias
Barrierwedge
Leftwardbias
Boxspring
spring
Barrier up
Barrier down
56
Possible Reversible Transitions
(Ignoring superposition states.)

• Catalog of all the possible transitions between
  definite states in these wells, both
  thermodynamically reversible not...

1states
1
1
1
leak
0
0states
0
leak
0
BarrierHeight
N
1
0
Direction of Bias Force
57
Erasing Digital Entropy

• Note that if the information in a bit-system is
  already entropy,
• Then erasing it just moves this entropy to the
  surroundings.
• This can be done with a thermodynamically
  reversible process, and does not necessarily
  increase total entropy!
• However, if/when we take a bit that is known, and
  irrevocably commit ourselves to thereafter
  treating it as if it were unknown,
• that is the true irreversible step,
• and that is when the entropy iseffectively
  generated!!

This state contains 1 bitof decomputable
information, in a stable, digital form
1
This state contains 1 bitof physical entropy,
but ina stable, digital form
?
1
0
0
Note This transformation is reversible!!
In these 3 configurations, there is no entropy
in the digital part of the state it has all
been pushed out into the environment.
N
0
58
Extropy

• Rather than repeatedly saying decomputable
  (i.e., compressible) information,
• A rather cumbersome phrase,
• Let us coin the term extropy (and sometimes use
  symbol X) to use for this concept.
• Name is chosen to connote the opposite of
  entropy.
• It has sometimes also been called negentropy.
• Since a systems total information content I X
  S,
• so we have X I - S. Thus ?X -?S whenever I
  is constant.
• We ignore previous and fuzzier meanings of the
  word extropy promoted by the Extropians.
• A certain trans-humanist organization.

59
Computation Speed Limits
60
The Margolus-Levitin Bound

• The maximum rate ?? at which a system can
  transition between distinguishable (orthogonal)
  states is ?? ? 4(E ? E0)/h
• where
• E average energy (expectation value of energy
  over all states, weighted by their probability)
• E0 energy of lowest-energy or ground state of
  system
• h Plancks constant (converts energy to
  frequency)
• Implication for computing
• A circuit node cant switch between 2 logic
  states faster than this frequency determined by
  its energy.

This is for pops,rate of nops ishalf as great.
61
Example of Frequency Bound

• Consider Lloyds 1 liter, 1 kg ultimate laptop
• Total gravitating mass-energy E of 9?1016 J
• Gives a limit of 5?1050 bit-operations per
  second!
• If laptop contains 2?1031 bits (photonic
  maximum),
• each bit can change state at a frequency of
  2.5?1019 Hz (25 EHz)
• 12 billion times higher-frequency than todays 2
  GHz Intel processors
• 250 million times higher-frequency than todays
  100 GHz superconducting logic
• But, the Margolus-Levitin limit may be far from
  achievable in practice!

62
More Realistic Estimates

• Most of the energy in complex stable structures
  is not accessible for computational purposes...
• Tied up in the rest masses of atomic nuclei,
• Which form anchor points for electron orbitals
• mass energy of core atomic electrons,
• Which fill up low-energy states not involved in
  bonding,
• of electrons involved in atomic bonds
• Which are needed to hold the structure together
• Conjecture Can obtain tighter valid quantum
  bounds on info. densities state-transition
  rates by considering only the accessible energy.
• Energy whose state-information is manipulable.

63
More Realistic Examples

• Suppose the following system is accessible1
  electron confined to a (10 nm)3 volume, at an
  average potential of 10 V above ground state.
• Accessible energy 10 eV
• Accessible-energy density 10 eV/(10 nm)3
• Maximum entropy in Smith bound 1.4 bits?
• Not clear whether Smith bound is really
  applicable to this case.
• Maximum rate of change 9.7 PHz
• 5 million typical frequencies in todays CPUs
• 100,000 frequencies in todays superconducting
  logics

64
Summary of Fundamental Limits