Title: Addressing the Funding Gap in Energy-Efficient Computing: Research Overview and Program Management Philosophy
1Addressing 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
2Overview 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
3Motivation
- The Coming Crisis in Computer Energy Efficiency
4Major 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.
5Computings 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
6Substantial 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
7These 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
8But, 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
9Why 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
10Trend 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
11An 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
12The Science
- Why Reversible Computing is Our Last, Great
Hope for Continuing to Improve Computing
Indefinitely
13The 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.
14A 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).
15An 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
16Irreversible 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
17Simple 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)
18Simulation 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
19Reversible 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.
20Some 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
21Important 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
22The 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
23Why Im Here
- My vision of CCF, EMT, and how I and my field fit
into it
24Areas 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
25Some 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
26What 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
27Some 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?
28What 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
29What 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.
30New 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
31New 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.
32Some 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
33Some 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.
34Program 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
35Educational 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
36Conclusion
- 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
37finis
- End of Presentation Extra Slides Follow
38Goals 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
39Structure 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
40Everyone 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!
41The 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.
42Proof 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
43Types 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
44Physics 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.
45Reversible/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.
46Reversible 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)
47Conventional 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
48In-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
49In-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
50Early 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
51The 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.
52Adiabatic 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
53The 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.
54Circuit 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!
55Conditionally 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
56Reversible 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
57Semiconductor 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
58The 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
59Fredkin 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
60Requirements 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.
61MEMS 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)
62MEMS 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)
63Would 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!
64Some 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
65Bennetts 1989 Algorithmfor Worst-Case
Reversiblization
k 3n 2
k 2n 3
66Worst-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
68Device-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
69Plenty 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
70One 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!)
71A 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