Addressing the Funding Gap in Energy-Efficient Computing: Research Overview and Program Management Philosophy

1
Addressing the Funding Gap in Energy-Efficient
Computing Research Overview and Program
Management Philosophy

• By Michael P. FrankPresented to the National
  Science FoundationDirectorate for Computer
  Information Science EngineeringComputer
  Communication Foundations (CCF) DivisionMonday,
  July 10, 2006

2
Overview of Talk

• Motivation
• The Looming Energy Efficiency Crisis in Computing
• and the related Funding Gap between government
  industry
• The Science
• Why something called Reversible Computing is
  really Our Only Hope for solving the problem
• And why we need to start major research on it
  now!
• Why Im Here
• Convey my vision of CCF, the EMT program and how
  the field of Reversible Computing fits into them
• Ideas on how I would help run the EMT program

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Motivation

• The Coming Crisis in Computer Energy Efficiency

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Major Motivation of my WorkThe Energy
Efficiency Crisis

• The bulk of past improvements in practical
  computer performance have been fundamentally
  enabled by steady improvements in the energy
  efficiency of computation
• Defined as the number of useful computational
  operations performed per unit of available energy
  dissipated into the form of waste heat
• Unfortunately, an end to the past trend of steady
  energy efficiency improvements is now clearly
  within sight
• Designs at many levels (devices, circuits,
  architectures, algorithms) for conventional
  computing are rapidly converging towards optimal
  design-point asymptotes, within a few-decade
  time-frame
• Beyond which substantial further progress will
  not be possible, at least not within the
  conventional classical, irreversible computing
  paradigm
• To circumvent the crisis, a radical paradigm
  shift in our models and structures for
  computation is required!
• I will show why reversible computing will be an
  essential part of this.

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Computings Rapid Climb

• The raw performance efficiency characteristics
  of our information processing technologies
  (computing, storage, communication) have been
  improving at a steady, exponentially increasing
  rate over time, for at least the past 50 years
• Due to Moores Law (integration scale of
  electronics doubles every 1-2 years) and related
  technology trends
• Performance trends also span multiple pre-IC
  technologies (vacuum tubes, relays, etc.) going
  back 100 years or more
• Each generation of performance improvements has
  reliably led to significant new
  information-processing applications becoming
  practicable

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Substantial Societal Impact

• Economic measures of the nations ( worlds)
  economy, such as GDP, per-capita income, and
  standard of living have also improved
  exponentially (although at slower rates) over
  this same period
• Its clear that a substantial portion of these
  gains was made possible by the introduction of
  new IT applications, itself made possible by raw
  technology improvements
• Nearly every major industry today has relied on
  digital/ electronic technologies for a
  substantial portion of the productivity gains it
  has made over the last few decades
• Effected either directly, or indirectly through
  its suppliers

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These historical observations raise an important
concern

• We can arguably expect that the future rate of
  growth of the entire world economy will
  substantially depend on future trends in
  information technology efficiency
• I.e., will our raw technologycapabilities
  flatten out,continue improvingsteadily, or
  accelerate even faster than before?

logefficiency
now
decade
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But, a Severe Problem

• The energy efficiency (useful operations
  performed per unit energy dissipated) of all
  conventional information processing technologies
  will flatten out within the next few decades
• This is true for fundamental and absolutely
  irrefutable physical reasons! (To be discussed)
• As a consequence, the cost efficiency (ops
  performed per unit cost) and thus practical
  performance (e.g., FLOPS per dollar of annual
  operating budget) of systems will also flatten!
• This is assuming only that the economic cost of
  energy will not soon enter a new era of rapid
  exponential decay
• Which seems unlikely since, at present, energy
  costs are rising
• If this flattening happens, it can be expected
  to have a substantial braking effect on the
  entire world economy!
• This would be an extremely negative outcome,
  which we should try our best to avoid at all
  costs

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Why Energy Efficiency of Conventional Computing
Must Flatten

• The potential energy efficiency gains from all
  conventional sources are limited For example
• Decrease logic signal energy by lowering logic
  voltages
• This has already reached a practical limit of on
  the order of 1V going to much lower voltages
  leads to excessive FET energy leakage
• Also, signal energy is subject to thermodynamic
  limits to be discussed
• Eliminate speculative execution and other
  unnecessary CPU activity
• Soon, energy dissipation becomes dominated by
  necessary activity
• Turn off unused functional units when not in use
  to avoid unnecessary power dissipation from
  leakage currents
• Soon, power is dominated by active switching in
  units that are in use
• Replace algorithms for general-purpose CPUs with
  FPGA configurations or special-purpose
  architectures
• This is quite helpful, but typically yields at
  most 100x savings
• Find new high-level algorithms that require fewer
  total operations
• This is great when possible, but as our
  algorithms improve, significantly better
  algorithms become harder and harder to find

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Trend of Minimum Transistor Switching Energy
Based on Data from International Technology
Roadmaps for Semiconductors
Node numbers(nm DRAM hp)
Historical trendline
Conservative industry targets
CV2/2 gate energy, Joules
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An Urgent Scientific Need

• Given the above considerations, I would say that
  one of the most important basic research issues
  that our society needs the field of computer
  science engineering to address is to find a
  definitive answer to the following question
• Can the introduction of new alternative,
  unconventional computing paradigms (such as
  reversible, quantum, and bio-inspired computing)
  realistically prevent or forestall the
  flattening of the information technology curve?
• And if so, how exactly can this work?
• My vision is that answering this question should
  be a primary scientific mission of the EMT
  program.
• Although other applications are also important

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The Science

• Why Reversible Computing is Our Last, Great
  Hope for Continuing to Improve Computing
  Indefinitely

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The von Neumann-Landauer (VNL) Bound

• Physical theorem To lose, obliviously erase, or
  otherwise irreversibly forget 1 bits worth of
  known information involves/requires the eventual
  dissipation of at least kBT ln 2 amount of free
  energy to heat in an external environment at some
  temperature T.
• kB here is Boltzmanns constant, 1.3810-23 J/K
  in energy/temperature units
• First alluded to by John von Neumann, 1949
  clarified and proven by Rolf Landauer, 1961.

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A simple proof of the VNL bound

• Heres a simple proof, from basic thermodynamic
  facts known for gt100 years!
• If known information becomes unknown, this is (by
  defn) an increase of entropy.
• Because entropy is simply unknown physical
  information.
• And, all information that is accessible to us is
  physical information anyway.
• Standard units of information and entropy are
  simply logarithmic units
• 1 bit log 2 ?b.logb2 (indefinite logarithm
  object), Boltzmanns constant kB log e
• Therefore, in units of Boltzmanns constant, 1
  bit kB(log 2/log e) kB ln 2
• Thus, the loss (forgetting) of 1 bit is, by
  definition, the very same thing as an increase of
  entropy by the amount kB ln 2.
• Once entropy is created, it can never be
  destroyed (2nd law of thermodynamics)
• This follows from the micro-scale reversibility
  of basic laws of (today quantum) mechanics
• As entropy builds up in a system, its temperature
  rises.
• To operate sustainably without eventual meltdown,
  
• The entropy generated must be expelled to an
  external environment.
• To add entropy S to an environment at temperature
  T requires adding energy E ST to that
  environment - this is the very definition of
  thermodynamic temperature!
• Thus, to forget a bit (i.e., permanently expel it
  into the environment) requires that we must
  eventually permanently commit energy kBT ln 2 to
  the environment (as heat).

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An Essential Element of Future Paradigms
Reversible Computing

• Basic idea (R. Landauer, 1961 C. Bennett,
  1973)
• Fundamental physics suggests that in principle
  there is no limit to the energy efficiency of
  computing technologies, although this is true
  only to the extent that we avoid performing
  irreversible operations that discard information
  during the computing process
• But, it seems that with sufficient engineering
  effort, we can in principle approach, as closely
  as we care to, the limit of a reversible computer
  that discards no information and dissipates no
  energy
• Our practical aim is not zero energy, just
  continued steady reductions!
• Present status of reversible computing
• Potential advantages/tradeoffs are reasonably
  well understood
• Models early prototypes exist, but no practical
  systems yet
• Of interest to other clusters Implementing this
  notion would eventually impact computer
  engineering CS at all levels!
• From low-level physical device requirements up
  through circuit design, theory, architecture,
  languages, algorithms

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Irreversible vs. Reversible Digital Operations

• A typical irreversible digital operation
• Regardless of the previous digital contents x of
  some circuit node or memory cell, destructively
  overwrite it with a given new value y.
• A closely corresponding, but reversible
  operation
• Reversibly transform the old physical state
  representing x in place to a new state the new
  value y.
• The semantic difference is that the 2nd op can
  only be done if the old value x is known
• This means, it can be reconstructed based on the
  new value y together with other available
  information.
• This restricts the kinds of replacements that can
  be done reversibly
• e.g., cant replace two bits a,b with the product
  ab and 1 other bit

x
y
bit bucket
y
x
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Simple Electronic Implementations

• Irreversible CLEAR (set to 0) operation
• Without knowing if there is charge on node N,
  connect it to ground (logic 0 reference level)
• The stored information is lost and the entire
  associated node energy E is dissipated to heat!

• Reversible CLEAR(change from 1 to 0)
• Given that N contains a 1, we connect it to a
  source that goes from 1 to 0 over time t gt tc
• Only a fraction tc/t of the node energy E is
  dissipated,
• tc 2RC is a time constant
• R resistance of path
• C capacitance of node

N
N
Switch open
N
Switch closed
1
Variablesource
0
Node is charged upwith an amount E
ofelectrostaticenergy
R
Node dischargessuddenly,all info energy
arefully lost
t
C
Charge Q (2EC)1/2 flows out in a controlled way
over time t, dissipation Ediss I2Rt Q2R/t
E(2RC/t)
(Adiabatic charge transfer)
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Simulation Results (Cadence/Spectre)

• Graph shows power dissipation vs. frequency
• in 8-stage shift register.
• At moderate frequencies (1 MHz),
• Reversible uses lt 1/100th the power of
  irreversible!
• At ultra-low power (1 pW/transistor)
• Reversible is 100 faster than irreversible!
• Minimum energy dissip. per nFET is lt 1 eV!
• 500 lower than best irreversible!
• 500 higher computational energy efficiency!
• Energy transferred is still 10 fJ (100 keV)
• So, energy recovery efficiency is 99.999!
• Not including losses in power supply, though

2LAL Two-level adiabatic logic (invented at UF,
00)
1 nJ
100 pJ
Standard CMOS
10 aJ
10 pJ
1 aJ
1 pJ
Energy dissipated per nFET per cycle
1 eV
100 fJ
2V
100 zJ
2LAL 1.8-2V
1V
10 fJ
10 zJ
0.5V
0.25V
kT ln 2
1 fJ
1 zJ
100 aJ
100 yJ
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Reversible and/or Adiabatic VLSI Chips Designed
_at_ MIT, 1996-1999
By EECS grad students Josie Ammer, Mike Frank,
Nicole Love, Scott Rixner,and Carlin Vieri under
CS/AI lab members Tom Knight and Norm Margolus.
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Some Important Results in Reversible Computing
So Far

• Landauer (IBM) 1961
• The von Neumann limit of kT ln 2 energy
  dissipation per bit operation only holds for
  irreversible operations.
• Lecerf 1963, Bennett (IBM) 1973
• Computers that use only reversible operations are
  still Turing universal.
• Fredkin Toffoli (MIT), 1980
• Reversible computers can be implemented in an
  idealized classical physical model.
• Feynman (CalTech), 1982
• Reversible computers can be implemented in a
  simple quantum physical model.
• This paper eventually spawned the field of
  quantum computing
• Younis Knight (MIT), 1993
• Pipelined, sequential logic circuits can be
  implemented in fully-reversible CMOS.
• This paper helped to spawn the field of adiabatic
  circuits
• MIT Pendulum Project (Ammer, Frank, Knight, Love,
  Margolus, Rixner, Vieri), 1994-1999
• Designed implemented fully reversible
  programmable circuits, general-purpose RISC
  architectures, high-level programming languages,
  and algorithms for a wide variety of classical CS
  problems
• Frank (MIT) 1997-1999
• When physical constraints are accounted for,
  reversible computers offer asymptotically lower
  energy, cost, and time complexity for broad
  classes of problems than conventional machines.
• Frank (UF) 2000-2002
• The advantages of reversible computing over
  conventional computing increase as small
  polynomials of the underlying technology
  characteristics The trends show reversible
  winning within decades for machines at usual
  scales

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Important Open Research Challenges in Reversible
Computing

• Fundamental research on practicability of
  reversible computing
• (Physics) Can we invent post-transistor devices
  with lower leakage and energy coefficients?
• This research requires cross-disciplinary
  collaboration with physicists
• (Engineering) Can we tailor physical mechanisms
  to precisely execute complex trajectories
  (computations) with high energy-recovery
  efficiency?
• E.g. efficient resonators and power-clock
  distribution systems driving adiabatic logic.
  Collaboration with extremely skilled EEs is
  needed
• (Structures) Can we design mostly-reversible
  architectures with low overhead for practical
  special-purpose applications, at least?
• Existing general-purpose reversible architectures
  are highly suboptimal
• (Theory) Can we reversibly emulate general
  irreversible algorithms with less space-time
  complexity overhead than presently known?
• Oracle-based results suggest not, but more work
  is needed

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The Funding Gap inEnergy-Efficient Computing

• As a proposal writer, Ive found that reversible
  computing falls into a rather awkward, in-between
  position
• Because it aims to help a broad range of
  practical applications, and is well-motivated by
  basic physics, many scientists who evaluate RC
  proposals say it seems too practical to receive
  basic research funding, they expect its
  development should be funded by industry.
• Yet, because RC is high-risk, very disruptive,
  and probably will take much longer than
  industrys traditional 10-year lab-to-fab time
  lag to develop and broadly adopt, industry has
  largely ignored it, in favor of more short-term
  approaches to save energy
• The major risk that society faces in allowing
  this funding gap to persist is that if industry
  steps in too late, then workable, practical
  implementations of RC might not be ready in time
  to prevent performance growth from stalling
• If there is even a brief stall, the loss of
  momentum could breed pessimism and choke off
  industrys will to continue innovating

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Why Im Here

• My vision of CCF, EMT, and how I and my field fit
  into it

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Areas Covered by CCF

• Emerging Models and Technologies (EMT)
• Paradigms Nanocomputing, quantum computing,
  biologically inspired computing
• I would add reversible computing to this list
• Founds. of Comp. Procs. Artifs. (FCPA)
• Structures Programming languages, computer
  architecture, VLSI design
• Theoretical Foundations (TF)
• Theory Models of computation, complexity,
  parallelism, algorithms, information theory

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Some Highlights of My Related Educational
Background

• Early exposure to nanotech/nanocomputing concepts
• Nanotechnology course, K. Eric Drexler, Stanford,
  1988
• Solid general background in CS theory AI
• BS in Symbolic Systems, Stanford, 1991
• MS in EECS on Decision-Theoretic techniques in
  AI, MIT, 1994
• Ph.D. proposal on DNA-based computing
• MIT Lab for CS, 94-95
• Fairly early exposure to Quantum Computing
• Reviewed the field for MIT EECS Ph.D. area exam,
  1995
• Ph.D. minor in conventional CMOS VLSI design
• Designed had fabbed several chips, for courses
  Ph.D. work
• Ph.D. work on Reversible Computing
• Included development of nanocomputing models,
  complexity theory, architectures, programming
  languages, VLSI design

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What I See As Some General Research Questions
Behind EMT

• What are the fundamental physical limits of
  present future information processing
  technologies?
• As opposed to the more abstract, algorithmic
  kinds of limits addressed by traditional
  theoretical CS
• What fundamental changes to our underlying
  models/paradigms of computation may we need in
  order to fully harness emerging technologies?
• New models based on physics (or chemistry,
  biology?)
• How can practical considerations help to guide
  our exploration of the emerging technology
  concepts?
• E.g., concerns with (at least estimates of)
  real-world cost, performance, energy efficiency,
  reliability, ease of use

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Some Cross-Cutting Questions to other areas of
CCF

• Cross-cutting to FCPA cluster
• What would the emergence of new computing
  paradigms require in terms of new architectures,
  programming languages, HW design tools?
• Cross-cutting to TF cluster
• What impacts do emerging technologies have on
  theoretical CS areas such as models of
  computation, complexity theory, algorithm design,
  and parallel computing?

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What are the Fundamental Physical Limits of
Computing?

• Fundamental laws of physics impose a variety of
  universal limits that hold true in all physically
  possible information processing technologies
• Thermodynamic von Neumann/Landauer (VNL) lower
  bound of kT ln 2 (18 meV at room temperature) on
  energy dissipated per known bit that is discarded
  into a temperature-T environment.
• However, this one could be avoided via reversible
  computing
• Quantum performance limit (Margolus-Levitin
  bound) of at most a rate 2E/h (hPlancks
  constant) of useful bit operations in any
  device with an active energy of E.
• This limit applies even to reversible quantum
  computers!
• There are also fundamental physical limits on
  information density and bandwidth, but I wont
  get into those here

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What Changes to Our Models/Paradigms are Needed?

• Two of the most important new paradigms
• Reversible computing teaches us that overcoming
  the energy-efficiency crisis will eventually
  require an emphasis on reversible operations,
  impacting increasingly higher levels throughout
  computing.
• Quantum computing teaches us that the fastest
  known algorithms for certain classes of problems
  require machines that provide the ability to
  perform uniquely quantum operations.

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New Paradigms for Computing

• Reversible computing aims to directly circumvent
  the energy efficiency problem through the use of
  energy-conserving physical mechanisms for
  information processing
• Quantum computing aims for dramatic algorithmic
  improvements for some types of problems, using
  shortcuts through state space made possible by
  nonclassical operations
• Bio-inspired computing broadly includes
• In vivo biological computing, e.g., bacteria
  genetically engineered to incorporate custom gene
  expression regulation networks
• In vitro biochemistry-based computing such as DNA
  computing and related approaches
• In silico but still biologically-inspired
  techniques such as digital analog neural
  networks, other analog approaches, neuromorphic
  computing, etc

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New Paradigms in Relation to What I see as EMTs
Mission

• Bio-inspired computing is interesting, but
  generally incapable of superseding the limits of
  conventional technology by very much
• All realistic bio-inspired approaches could be
  simulated by conventional parallel digital
  machines with (at most) modest constant-factor
  overheads
• The motivation for bio-inspired computing must
  come from other directions
• Quantum computing is nice if it can be made to
  work, but as far as we know, it is limited in its
  applicability to relatively narrow classes of
  problems (e.g., hidden subgroup, modest gains for
  search)
• Its potential economic impact is therefore only a
  small fraction of that for all leading-edge
  computing in general
• Research that aims to broaden its applicability
  is potentially worthwhile
• Reversible computing is the only unconventional
  paradigm that might possibly break down the
  roadblocks to indefinite future improvement of
  computer efficiency and practical performance in
  general applications
• Its future economic value is thus potentially
  unlimited
• However, it is difficult to do, and still in its
  infancy! Much research is needed.

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Some Other Motivations for Paradigms Covered by
EMT

• Bio-inspired computing
• In vivo computing Self-reproducing,
  self-organizing microbial systems for various
  clinical or industrial applications
• In vitro computing Self-assembly of
  nanostructures
• Neural networks Applications in machine learning
• Analog electronics Low-power signal processing
• Quantum computing
• Fast factoring etc. for cryptanalysis of PK
  cryptosystems
• Strong information security via quantum
  cryptography
• Fast, flexible, accurate simulation of quantum
  physical systems
• Reversible computing
• Reversible logic is already used in quantum
  computing, and has a few possible applications in
  other areas of CS
• Security auditable/verifiable computation,
  resilient systems
• Transaction rollback for concurrent systems
• May conceivably provide useful angles for
  tackling complexity-theory questions
• e.g., FACTORING?P iff ? a poly-time zero-garbage
  reversible alg. to multiply primes

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Some Important Research Challenges in Quantum
Computing

• Important experimental physics challenges
• Develop new experimental setups for prototype
  quantum computers that can effectively suppress
  decoherence to the threshold for fault-tolerance
• To enable more rapid improvement of machine sizes
• Develop effective physical architectures for
  efficient qubit transfer execution of parallel
  quantum circuits
• Important theory challenges
• Better characterize the limits of applicability
  of quantum algorithms
• Find major new categories of applications beyond
  the scope of the standard hidden subgroup /
  unstructured search algorithms
• Resolve major open issues in quantum complexity
  theory
• Comparisons between BQP vs. BPP and NP, etc.

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Program Administration Ideas

• My personal program management philosophy
• Hands-on leadership, guiding steering the
  work of proposers reviewers based on my vision
  and understanding of the programs mission and
  the scientific needs of the fields that it
  touches on
• Clarify the vision and goals of the funding
  program up-front with a technical white paper
  surveying important open scientific issues
• Include motivation for and summaries of important
  open research problems, with references to the
  literature
• Encourage proposal writers to address the listed
  issues, or else to thoroughly motivate their own
  alternative directions
• Proactively seek out researchers whose
  background, skills, and research interests seem
  to mesh well with the clusters mission and
  vision
• and encourage them to submit proposals to the
  program
• Encourage review panel members to carefully
  consider the quality thoroughness of the
  motivation section when evaluating the scientific
  merit of proposals
• IMHO, too much of todays research is not
  sufficiently well-motivated

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Educational Component

• Strongly encourage proposers to include
  educational activities in their proposals,
  including
• Organizing of conferences
• Writing of technical books textbooks
• Writing of introductory books for popular
  audiences
• Even encourage submission of proposals for
  activity that is primarily educational in nature
• There is an education gap in the areas I
  discussed also
• Especially in reversible computing, which is
  still little known
• Emphasize the need for educational materials that
  have a strong interdisciplinary perspective
• E.g., integrating CS, EE, physics issues

36
Conclusion

• Among the various unconventional computing
  technologies, there are strong reasons to believe
  that reversible computing has the greatest
  potential to make an enormous, vital, broad, and
  timely economic impact in coming decades
• Yet, compared to areas such as DNA, quantum, nano
  and bacterial computing, it has received by far
  the least attention and funding!
• One of my main motivations for working in
  reversible computing has been to correct the
  imbalance between the underlying importance of
  and popular attention to this field
• However, my influence as a lone researcher in
  the trenches is limited No programs support
  this presently unfashionable field
• I hope in my position at EMT to help to finally
  bring some much-needed funding and attention to
  this orphaned area, and help guide research in
  new, productive directions
• While continuing support for well-motivated
  projects in other areas

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finis

• End of Presentation Extra Slides Follow

38
Goals of Presentation

• Convey my vision for research education
  advancements in areas covered by CCF
• Emerging Models Technologies for Comp.
• Foundations of Computing Processes Artif.
• Theoretical Foundations
• Discuss what I see as major challenges
• some ideas on how to address them
• Review my own scientific activities
• briefly survey some related areas

39
Structure of Talk

• Briefly introduce what I believe are some
  important scientific questions that CCF can work
  to address
• Both within EMT, and potentially cross-cutting to
  other clusters within CCF, even to other
  directorates
• Engineering, physical sciences
• Summarize some of my own past research that
  relates to these questions
• Work done at MIT, Univ. of Florida, and Florida
  State
• List some related research challenges
• Present some ideas/strategies for administration
  of CCF programs so as to facilitate scientific
  progress on these issues

40
Everyone Has It All Wrong!

• As the talk proceeds,
• Ill explain (in the proud MIT tradition) why
  most of the rest of the world is thinking about
  the future of computing in a completely
  wrong-headed way.
• In particular,
• The Low-Power Logic Circuit Designers have it all
  wrong!
• The Semiconductor Process Engineers have it all
  wrong!
• (Most) Device Physicists have it all wrong!

41
The von Neumann-Landauer (VNL) principle

• John von Neumann, 1949
• Claim The minimum energy dissipated per
  elementary (binary) act of information is kT ln
  2.
• No published proof exists only a 2nd-hand
  account of a lecture
• Rolf Landauer (IBM), 1961
• Logically irreversible (many-to-one) bit
  operations must dissipate at least kT ln 2
  energy.
• Paper anticipated but didnt fully appreciate
  reversible computing
• One proper (i.e. correct) statement of the
  principle
• The oblivious erasure of a known logical bit
  generates at least k ln 2 amount of new entropy.
• Releasing into environment at T requires kT ln 2
  heat emission.

42
Proof of the VNL Principle

• The principle is occasionally questioned, but
• Its truth follows absolutely rigorously (and even
  trivially!) from rock-solid principles of
  fundamental physics!
• (Micro-)reversibility of fundamental physics
  implies
• Information (at the microscale) is conserved
• I.e., physical information cannot be created or
  destroyed
• only transformed via reversible, deterministic
  processes
• Thus, when a known bit is erased (lost,
  forgotten) it must really still be preserved
  somewhere in the microstate!
• But, since its value has become unknown, it has
  become entropy
• Entropy is just unknown/incompressible information

43
Types of Dynamical Processes

• These animations illustrate how states transform
  in their configuration space, in
• A nondeterministic process
• One-to-many transformations
• An irreversible process
• Many-to-one transformations
• Nondeterministic and irreversible
• Deterministic and reversible
• One-to-one transformations only!

WE ARE HERE
44
Physics is Reversible!

• Despite all of the empirical phenomenology
  relating to macro-scale irreversibility, chaos,
  and nondeterministic quantum events,
• Our most fundamental and thoroughly-tested modern
  models of physics (e.g. the Standard Model) are,
  at bottom, deterministic reversible!
• All of the observed nondeterministic and
  irreversible phenomena can still be explained
  within such models, as emergent effects.
• Although classical General Relativity is argued
  by some researchers to have certain irreversible
  aspects,
• The general consensus seems to be that well
  eventually find that the correct theory of
  quantum gravity will be reversible.

45
Reversible/Deterministic Physics is Consistent
with Observations

• Apparent quantum nondeterminism can validly be
  understood as an emergent phenomenon, an expected
  practical result of permanent wavefunction
  splitting
• As illustrated e.g. in the many worlds and
  decoherent histories pictures
• Even if a quantum wavefunction does not split
  permanently, its evolution in a large system can
  quickly become much too complex to track within
  our models
• Thus we resort to using reduced density
  matrices, which discard some knowledge
• The above effects, plus imprecision in our
  knowledge of fundamental constants, result in
  some practical unpredictability even for
  microscale systems
• Thus entropy, for all practical purposes, tends
  to increase towards its maximum
• Chaos (macro-scale nondeterminism) occurs when
  entropy at the microscale infects our ability to
  forecast the long-term evolution of macroscopic
  variables
• A necessary consequence of the computation-univers
  ality of physics?
• Meanwhile, averaging of many high-entropy
  microscopic details results in a smoothing
  effect that leads to irreversible evolution of
  macro-variables.

46
Reversible Computing

• Wed like to design mechanisms that compute while
  producing as little entropy as possible
• In order to minimize consumption of free energy /
  emission of heat to the environment
• Losing known information necessarily results in a
  minimum k ln 2 entropy increase per bit lost, so
• Lets consider what we can do using logically
  reversible (one-to-one) operations that dont
  lose information.
• Such operations are still computationally
  universal!
• Lecerf (1963), Bennett (1973)

47
Conventional Gate Operations are Irreversible
(even NOT!)

• Consider a computer engineers (i.e., real
  world!) Boolean NOT gate (a.k.a. logical
  inverter)
• Specified function Destructively overwrite
  output nodes value with the logical complement
  of the input!

Hardwarediagram
Space-time logic networkdiagram (not the same
thing!!)
New in
in
Oldin
Twodifferentphysicallogicnodes
Inverteroperation
Invertergate
Oldout
New out
out
time
48
In-Place NOT (Reversible)

• Computer scientists (i.e., somewhat
  fictionalized!) in-place logical NOT operation
• Specified operation Replace a given logic
  signal with its logical complement.
• People occasionally confuse the irreversible
  inverter operation with a reversible in-place NOT
  operation
• The same icon is sometimes used in spacetime
  diagrams

time
time
in
out
old bit
new bit
49
In-Place Controlled-NOT (cNOT)

• Specified function Perform an in-place NOT on
  the 2nd bit if and only if the 1st bit is a 1.
• Equiv., replace 2nd bit with XOR of 1st 2nd bits

Before Before After After
C D C D
0 0 0 0
0 1 0 1
1 0 1 1
1 1 1 0
Transitiontable
control
old data
new data
time
50
Early Universal Reversible Gates

• Controlled-controlled-NOT (ccNOT)
• A.k.a. Toffoli gate
• Perform cNOT(b,c) iff a1.
• Equiv., c c XOR (a AND b)
• Controlled-SWAP (cSWAP)
• A.k.a. Fredkin gate
• Swap b with c iff a1.
• Conserves 1s

A
B
C
A
B
C
51
The Adiabatic Principle

• Applied physicists know that a wide class of
  physical transformations can be done
  adiabatically
• From Greek adiabatos, It shall not be passed
  through
• Used to mean, no passage of heat through an
  interface separating subsystems at different
  temperatures
• Newer, more general meaning No increase of
  entropy
• Of course, exactly zero entropy increase isnt
  practically doable
• In practice, adiabatic is used to mean that the
  entropy generation scales down proportionally as
  the process takes place more gradually.
• The general validity of this 1/t scaling relation
  is enshrined in the famous adiabatic theorem of
  quantum mechanics.

52
Adiabatic Charge Transfer
Q

• Consider passing a total quantity of charge Q
  through a resistive element of resistance R over
  time t via a constant current, I Q/t.
• The power dissipation (rate of energy diss.)
  during such a process is P IV, where V IR is
  the voltage drop across the resistor.
• The total energy dissipated over time t is
  therefore E Pt IVt I2Rt (Q/t)2Rt
  Q2R/t.
• Note the inverse scaling with the time t.
• In adiabatic logic circuits, the resistive
  element is a switch.
• The switch state can be changed by other
  adiabatic charge transfers.
• In simple FET-type switches, the constant factor
  (energy coefficient) Q2R appears to be subject
  to some fundamental quantum lower bounds.
• However, these are still rather far away from
  being reached.

R
53
The Low-Power Design community has it all wrong!

• Even (most of) the ones who know about adiabatics
  and even many who have done extensive amounts of
  research on adiabatic circuits still arent doing
  it right!
• Watch out! 99 of the so-called adiabatic
  circuit designs published in the low-power design
  literature arent truly adiabatic, for one reason
  or another!
• As a result, most published results (and even
  review articles!) dramatically understate the
  energy efficiency gains that can actually be
  achieved with correct adiabatic design.
• Which has resulted in (IMHO) too little serious
  attention having been paid to adiabatic
  techniques.

54
Circuit Rules for True Adiabatic Switching

• Avoid passing current through diodes!
• Crossing the diode drop leads to irreducible
  dissipation.
• Follow a dry switching discipline (in the relay
  lingo)
• Never turn on a transistor when VDS ? 0.
• Never turn off a transistor when IDS ? 0.
• Together these rules imply
• The logic design must be logically reversible
• There is no way to erase information under these
  rules!
• Transitions must be driven by a quasi-trapezoidal
  waveform
• It must be generated resonantly, with high Q
• Of course, leakage power must also be kept
  manageable.
• Because of this, the optimal design point will
  not necessarily use the smallest devices that can
  ever be manufactured!
• Since the smallest devices may have insoluble
  problems with leakage.

Importantbut oftenneglected!
55
Conditionally Reversible Gates

• Avoiding VNL actually only requires that the
  operation be one-to-one on the subset of states
  actually encountered in a given system
• This allows us to design with gates that do
  conditionally reversible operations
• That is, they are reversible if certain
  preconditions are met
• Such gates can be built easily using ordinary
  switches!
• Example cSET (controlled-SET) and cCLR
  (controlled-CLR) operations can be implemented
  with a single digital switch (e.g. a CMOS
  transmission gate), with operation timing
  controlled by an externally-supplied driving
  signal
• These operations are conditionally reversible, if
  preconditions are met

Hardwareschematic
Hardwareicon
Space-time logic diagram
in
in
in
drive
drive
newout in
oldout 0
finalout 0
0?1
1?0
out
out
56
Reversible OR (rOR) from cSET

• Semantics rOR(a,b)if ab, c1.
• Set c1, if either a or b is 1.
• Reversible if initially ab ? c.
• Two parallel cSETs simultaneouslydriving a
  shared output busimplements the rOR operation!
• This is a type of gate composition that was not
  traditionally considered.
• Similarly, one can do rAND, and reversible
  versions of all Boolean operations.
• Logic synthesis with theseis extremely
  straightforward

Hardware diagram
a
c
b
Spacetime diagram
a
a
a OR b
0
c
c
b
b
57
Semiconductor Process Engineers have it all wrong!

• Everybody still thinks that smaller FETs
  operating at lower voltages will forever be the
  way to obtain ever more energy-efficient and more
  cost-efficient designs.
• But if correct adiabatic design techniques are
  included in our toolbox, this is simply not true!
• With good energy recovery, higher switching
  voltages (requiring somewhat larger devices)
  enable strictly greater overall energy
  efficiency! (and thus lower energy cost!)
• This is due to the suppression of FET leakage
  currents exponentially with Vq/kT.
• The hardware cost-performance overheads of this
  approach only grow polylogarithmically with the
  energy efficiency gains
• Over time, we can expect the overheads will be
  overtaken by competitively-driven per-device
  manufacturing cost reductions
• If devices better than FETs arent found,
• then I predict an eventual bounce in device
  sizes

58
The Need for Ballistic Processes

• In order to achieve low overall entropy
  generation in a complete system,
• Not only must the logic transitions themselves
  take place in an adiabatic fashion,
• but also the components that drive and control
  the signal levels and timing of logic transitions
  (power clocks) must proceed reversibly along
  the desired trajectory.
• Thus, we require a ballistic driving mechanism
• One that proceeds under its own momentum along
  a desired trajectory with relatively little
  entropy increase.
• Many concepts for such mechanisms have been
  proposed, but
• Designing a sufficiently high-quality power-clock
  mechanism remains the major unsolved problem of
  reversible computing

59
Fredkin and Toffolis (1980) Billiard-Ball Model

• 1st conceptual model of a ballistic physical
  computing process
• Perfectly rigid billiard balls bounce off walls
  each other in digitally-precise trajectories

• Shown to be capable of asymptotically efficient
  simulations of arbitrary reversible circuits in
  2D (extensible to 3D also)
• Its idealized it would be chaotically unstable
  in practice
• The addition of appropriate constraining
  mechanisms to prevent the balls from going off
  track or out of sync is viewed as a later step
• Zurek argued that analogous quantum processes can
  avoid the chaos

60
Requirements for Energy-Recovering Clock/Power
Supplies

• All of the known reversible computing schemes
  require the presence of a periodic and globally
  distributed signal that synchronizes and drives
  adiabatic transitions in the logic.
• For good system-level energy efficiency, this
  signal must oscillate resonantly and
  near-ballistically, with a high effective quality
  factor.
• Several factors make the design of a resonant
  clock distributor that has satisfactorily high
  efficiency quite difficult
• Any uncompensated back-action of logic on
  resonator
• In some resonators, Q factor may scale
  unfavorably with size
• Excess stored energy in resonator may hurt the
  effective quality factor
• Theres no reason to think that its impossible
  to do it
• But it is definitely a nontrivial hurdle, that we
  reversible computing researchers need to face up
  to, pretty urgently
• If we hope to make reversible computing practical
  in time to avoid an extended period of stagnation
  in computer performance growth.

61
MEMS Resonator Concept
Arm anchored to nodal points of fixed-fixed beam
flexures,located a little ways away, in both
directions (for symmetry)

z
y
Phase 180 electrode
Phase 0 electrode
Repeatinterdigitatedstructurearbitrarily
manytimes along y axis,all anchored to the
same flexure
x
C(?)
C(?)
0
360
0
360
?
?
(PATENT PENDING, UNIVERSITY OF FLORIDA)
62
MEMS Quasi-Trapezoidal Resonator 1st Fabbed
Prototype
(Funding source SRC CSR program)

• Post-etch process is still being fine-tuned.
• Parts are not yet ready for testing

Primaryflexure(fin)
Sensecomb
Drive comb
(PATENT PENDING, UNIVERSITY OF FLORIDA)
63
Would a Ballistic Computer be a Perpetual Motion
Machine?

• Short answer No, not quite!
• Hey, give us some credit here!
• Were hard-core thermodynamics geeks, we know
  better than that!
• Two traditional (and impossible!) kinds of
  perpetual motion machines
• 1st kind Increases total energy - Violates 1st
  law of thermo. (energy conservation)
• 2nd kind Reduces total entropy - Violates 2nd
  law of thermo. (entropy non-decrease)
• Another kind that might be possible in an ideal
  world, but not in practice
• 3rd kind Produces exactly 0 increase in
  entropy!
• Requires perfect knowledge of physical constants,
  perfect isolation of system from environment,
  complete tracking of systems global
  wavefunction, no decoherence, etc.
• What were more realistically trying to build in
  reversible computing is none of the above, but
  only the more modest goal of a For-a-long-time
  Motion Machine
• I.e., one that just produces as close to zero
  entropy (per op) as we can possibly achieve!
• It would coast along for a while, but without
  energy input, it would eventually halt
• Such a coasting machine can perform no net
  mechanical work in a complete cycle,
• But it can potentially do a substantial amount of
  useful computational work!

64
Some Results on Scalability of Reversible
Computers

• In a realistic physics-based model of computation
  that accounts for thermodynamic issues
• When leakage is negligible and heat flux density
  is bounded,
• Adiabatic machines asymptotically outperform
  irreversible machines (even per unit cost!) as
  problem sizes machine sizes are scaled up
• But, the absolute speedup when total system power
  is unrestricted grows only as a small polynomial
  with the machine size
• E.g., exponents of 1/36 or 1/18, depending on
  problem class
• The speedup per unit surface area or
  (equivalently) per unit power dissipation grows
  at a somewhat faster (but still gradual) rate
• E.g., with the 1/6 power of machine size
• Even when leakage is non-negligible,
• Adiabatic machines can still attain
  constant-factor (i.e., problem-size-independent)
  energy savings ( speedups at fixed power) that
  scale as moderate polynomials of the device
  characteristics
• E.g., roughly with the transistor on-off ratio to
  at least the 0.39 power
• Cost overheads from RC in these scenarios also
  grow, somewhat faster
• But, we can hope that device costs will continue
  to decline over time

65
Bennetts 1989 Algorithmfor Worst-Case
Reversiblization
k 3n 2
k 2n 3
66
Worst-Case Energy/Cost Tradeoff(Optimized
Bennett-89 Variant)
cost ? energy ?1.59
Spacetime cost blowup factor
Energy savings factor
k
n
67
(Most) Device Physicists have it all wrong!

• Unfortunately, Id say gt90 of papers published
  on new logic device concepts (whether based on
  CNTs, spintronics, etc.) either ignore or
  dramatically neglect the key issue of the energy
  efficiency of logic operations
• Even though, looking forward, this is absolutely
  the most crucial parameter limiting the practical
  performance of leading-edge computing systems!
• And, even the rare few device physicists who
  study reversible devices dont seem to be talking
  to the analog/RF/µwave engineers who might help
  them solve the many subtle and difficult problems
  involved in building extremely high-quality
  energy-recovering power-clock resonators

68
Device-Level Requirements for Reversible Computing

• A good reversible digital bit-device technology
  should have
• Low amortized manufacturing cost per device, d
• Important for good overall (system-level)
  cost-efficiency
• Low per-device level of static standby power
  dissipation Psb due to energy leakage,
  thermally-induced errors, etc.
• This is required for energy-efficient storage
  devices, especially
• but its still a requirement (to a lesser extent)
  in logic as well
• Low energy coefficient cEt Edissttr (energy
  dissipated per operation, times transition time)
  for adiabatic transitions between digital states.
• This is required in order to maintain a high
  operating frequency simultaneously with a high
  level of computational energy efficiency.
• And thus maintain good hardware efficiency (thus
  good cost-performance)
• High maximum available transition frequency fmax.
• This is especially important for applications in
  which the latency from inherently serial
  computing threads dominates total operating costs

69
Plenty of Room forDevice Improvement
Power per device, vs. frequency

• Recall, irreversible device technology has at
  most 3-4 orders of magnitude of
  power-performance improvements remaining.
• And then, the firm kT ln 2 (VNL) limit is
  encountered.
• But, a wide variety of proposed reversible device
  technologies have been analyzed by physicists.
• With preliminary estimates of theoretical
  power-performance up to 10-12 orders of magnitude
  better than todays CMOS!
• Ultimate limits are unclear.

.18µm CMOS
.18µm 2LAL
k(300 K) ln 2
Variousreversibledevice proposals
70
One Optimistic Scenario
40 layers, ea. w.8 billion activedevices,freq.
180 GHz,0.4 kT dissip.per device-op
e.g. 1 billion devices actively switching at3.3
GHz, 7,000 kT dissip. per device-op
Note that by 2020, there could be a factor of
20,000 difference in rawperformance per 100W
package. (E.g., a 100 overhead factor from
reversible design could be absorbed while still
showing a 200 boost in performance!)
71
A Call to Action

• The world of computing is threatened by permanent
  raw performance-per-power stagnation in 1-2
  decades
• We really should try hard to avoid this, if at
  all possible!
• A wide variety of very important applications
  will be impacted.
• Many more of the nations (and the worlds) top
  physicists and computer scientists must be
  recruited,
• to tackle the great Reversible Computing
  Challenge.
• Urgently needed A major new fundin