FAMU-FSU College of Engineering 6/19/06M. Frank, NSF/CISE/CCF job talk1 Addressing the Funding Gap...

6/19/06 M. Frank, NSF/CISE/CCF job talk 1

FAMUFAMU--FSUFSU College of EngineeringCollege of Engineering

Addressing the Funding Gap in Addressing the Funding Gap in Energy-Efficient Computing: Energy-Efficient Computing:

Research Overview and Research Overview and Program Management Program Management

PhilosophyPhilosophyBy Michael P. Frank

Presented to the National Science FoundationDirectorate for Computer & Information Science & Engineering

Computer & Communication Foundations (CCF) DivisionMonday, July 10, 2006


FAMU-FSU College of Engineering

Overview of TalkOverview 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 I’m 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



MotivationMotivation

The Coming Crisis in Computer Energy Efficiency



Major Motivation of my Work:Major Motivation of my Work:The Energy Efficiency CrisisThe 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.



Computing’s Rapid ClimbComputing’s 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 “Moore’s 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…



Substantial Societal ImpactSubstantial Societal Impact Economic measures of the nation’s (& world’s)

economy, such as GDP, per-capita income, and standard of living have also improved exponentially (although at slower rates) over this same period… It’s 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



These historical observations These historical observations raise an important concern…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 technology

capabilities flatten out,continue improvingsteadily, or accelerate even faster than before?

logefficiency

decade

now



But, a Severe Problem…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…



Why Energy Efficiency of Conventional Why Energy Efficiency of Conventional Computing Must FlattenComputing 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



Trend of Minimum Transistor Trend of Minimum Transistor Switching EnergySwitching Energy

fJ

aJ

zJ

Node numbers(nm DRAM hp)

CV

2/2

ga

te e

ne

rgy,

Jo

ule

s Historical trendline

Conservative industry targets

Based on Data from International Technology Roadmaps for Semiconductors



An Urgent Scientific NeedAn 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…



The ScienceThe Science

Why Reversible Computing is Our “Last, Great Hope” for Continuing to Improve

Computing Indefinitely



The von Neumann-Landauer The von Neumann-Landauer (VNL) Bound(VNL) Bound

Physical theorem: To lose, obliviously erase, or otherwise irreversibly forget 1 bit’s 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 Boltzmann’s constant, 1.38×10−23 J/K in

energy/temperature units First alluded to by John von Neumann, 1949;

clarified and proven by Rolf Landauer, 1961.



A simple proof of the VNL boundA simple proof of the VNL bound Here’s a simple proof, from basic thermodynamic facts known for >100 years!

If known information becomes unknown, this is (by def’n) 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 = λb.logb2 (indefinite logarithm object), Boltzmann’s constant kB = log e Therefore, in units of Boltzmann’s 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).



An Essential Element of An Essential Element of Future Paradigms: Reversible ComputingFuture 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…



Irreversible vs. Reversible Irreversible vs. Reversible Digital OperationsDigital 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., can’t replace two bits a,b with the product ab and 1 other bit

xy

bit bucket

xy



Variablesource

Simple Electronic ImplementationsSimple 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 > 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 NSwitch open

Node is charged up

with an amount E ofelectrostatic

energy

Switch closed

Node dischargessuddenly,all info & energy arefully lost

N

CR

Charge Q = (2EC)1/2 flows out in a controlled way over time t, dissipation Ediss = I2Rt = Q2R/t = E(2RC/t)

1

0t

(Adiabatic charge transfer)

Simulation Results (Cadence/Spectre)Simulation Results (Cadence/Spectre) Graph shows power

dissipation vs. frequency in 8-stage shift register.

At moderate frequencies (1 MHz), Reversible uses

< 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 < 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

1 nJ

100 pJ10 pJ

1 pJ

100 fJ

10 fJ

1 fJ

100 aJ

10 aJ

1 aJ

100 zJ10 zJ

1 zJ

kT ln 2

1 eV

Standard CMOS

2V1V0.5V

0.25V

2LAL 1.8-2V

Ene

rgy dissipated per nF

ET

per cycle

100 yJ

2LAL = Two-level adiabatic logic (invented at UF, ‘00)



Reversible and/or Adiabatic VLSI Reversible and/or Adiabatic VLSI Chips Designed @ MIT, 1996-1999Chips Designed @ 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.



Some Important Results in Some Important Results in Reversible Computing So FarReversible 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



Important Open Research Important Open Research Challenges in Reversible ComputingChallenges 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



The Funding Gap inThe Funding Gap inEnergy-Efficient ComputingEnergy-Efficient Computing

As a proposal writer, I’ve 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 industry’s 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 industry’s will to continue innovating…



Why I’m HereWhy I’m Here

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

into it



Areas Covered by CCFAreas 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…



Some Highlights of My Some Highlights of My Related Educational BackgroundRelated 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



What I See As Some General What I See As Some General Research Questions Behind EMTResearch 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…



Some Cross-Cutting Some Cross-Cutting Questions to other areas of CCF 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?



What are the Fundamental What are the Fundamental Physical Limits of Computing?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 (h=Planck’s 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 won’t get into those here…



New Paradigms for ComputingNew 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…



New Paradigms in Relation to New Paradigms in Relation to What I see as EMT’s Mission What I see as EMT’s 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.



Some Other Motivations for Paradigms Some Other Motivations for Paradigms Covered by EMTCovered 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., FACTORINGP iff a poly-time zero-garbage reversible alg. to multiply primes



Some Important Research Some Important Research Challenges in Quantum ComputingChallenges 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.



Program Administration IdeasProgram 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 program’s 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 cluster’s 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 today’s research is not sufficiently well-motivated



Educational ComponentEducational 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



ConclusionConclusion

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



finisfinisEnd of Presentation – Extra Slides Follow



Everyone Has It All Wrong!Everyone Has It All Wrong! As the talk proceeds,

I’ll 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!



The von Neumann-Landauer The von Neumann-Landauer (VNL) principle(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 didn’t 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.



Proof of the VNL PrincipleProof 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



Types of Dynamical ProcessesTypes 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



Physics is Reversible!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 we’ll eventually find that

the “correct” theory of quantum gravity will be reversible.



Reversible/Deterministic Physics is Reversible/Deterministic Physics is Consistent with ObservationsConsistent 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-universality of physics?

Meanwhile, averaging of many high-entropy microscopic details results in a “smoothing” effect that leads to irreversible evolution of macro-variables.



Reversible ComputingReversible Computing We’d 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… Let’s consider what we can do using logically reversible

(one-to-one) operations that don’t lose information. Such operations are still computationally universal!

Lecerf (1963), Bennett (1973)



time

Conventional Gate Operations are Conventional Gate Operations are Irreversible (even NOT!)Irreversible (even NOT!)

Consider a computer engineer’s (i.e., real world!) Boolean NOT gate (a.k.a. logical inverter) Specified function: Destructively overwrite output

node’s value with the logical complement of the input!

in

out

Oldin

Oldout

Hardwarediagram:

Space-time logic networkdiagram (not the same thing!!):

Newin

Newout

Twodifferentphysical

logicnodes

Invertergate

Inverteroperation



In-Place NOT (Reversible) In-Place NOT (Reversible)

Computer scientist’s (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

in out old bit new bit

time time



In-Place Controlled-NOT (cNOT)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

control

olddata

newdata

Before After

C D C D

0 0 0 0

0 1 0 1

1 0 1 1

1 1 1 0

Transitiontable

time



Early Universal Reversible GatesEarly Universal Reversible Gates

Controlled-controlled-NOT (ccNOT) A.k.a. Toffoli gate

Perform cNOT(b,c) iff a=1. Equiv., c := c XOR (a AND b)

Controlled-SWAP (cSWAP) A.k.a. Fredkin gate

Swap b with c iff a=1.

Conserves 1s

A

B

C

A

B

C



The Adiabatic PrincipleThe 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 isn’t 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.



Adiabatic Charge TransferAdiabatic Charge Transfer

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

Q



The The Low-Power DesignLow-Power Design community has it all wrong!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 aren’t doing it right!

Watch out! 99% of the so-called “adiabatic” circuit designs published in the low-power design literature aren’t 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.



Circuit Rules for Circuit Rules for True Adiabatic SwitchingTrue 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!



Conditionally Reversible GatesConditionally 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

drive

out

in

01 10old

out = 0

in

newout = in

finalout = 0

Hardwareschematic: Space-time logic diagram

Hardwareicon:

in

out

drive



Reversible OR (Reversible OR (rORrOR) ) from from cSETcSET

Semantics: rOR(a,b)::=if a|b, c:=1. Set c:=1, if either a or b is 1.

Reversible if initially a|b → ~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 these

is extremely straightforward…

c

b

a a’

b’

c’0 a OR b

a

b

c

Spacetime diagram

Hardware diagram



Semiconductor Process EngineersSemiconductor Process Engineers have it all have it all wrong!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 aren’t found, then I predict an eventual “bounce” in device sizes



The Need for Ballistic ProcessesThe 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



Requirements for Energy-Recovering Requirements for Energy-Recovering Clock/Power SuppliesClock/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

There’s no reason to think that it’s 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.



Movingplate

Moving metal plate support arm/electrode

MEMS Resonator ConceptMEMS Resonator Concept

Range of Motion

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

Phase 0° electrode Phase 180° electrode

θ0° 360°

C(θ) C(θ)

θ0° 360°

… Repeatinterdigitated

structurearbitrarily many

times along y axis,all anchored to the

same flexure

x

yz

(PATENT PENDING, UNIVERSITY OF FLORIDA)



MEMS Quasi-Trapezoidal Resonator: 1MEMS Quasi-Trapezoidal Resonator: 1stst Fabbed PrototypeFabbed Prototype

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

(PATENT PENDING, UNIVERSITY OF

FLORIDA)

Drive comb

Sensecomb

Primaryflexure

(fin)

(Funding source: SRC CSR program)



Would a Ballistic Computer Would a Ballistic Computer be a Perpetual Motion Machine?be a Perpetual Motion Machine?

Short answer: No, not quite! Hey, give us some credit here!

We’re 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 system’s global wavefunction, no decoherence, etc.

What we’re 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!



Some Results on Scalability Some Results on Scalability of Reversible Computersof 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



Bennett’s 1989 AlgorithmBennett’s 1989 Algorithmfor Worst-Case “Reversiblization”for Worst-Case “Reversiblization”

k = 2n = 3

k = 3n = 2

Spacetime cost b

lowup factor

Energy savings factor

kn

Worst-Case Energy/Cost TradeoffWorst-Case Energy/Cost Tradeoff(Optimized Bennett-89 Variant)(Optimized Bennett-89 Variant)

cost energy 1.59



(Most) (Most) Device PhysicistsDevice Physicists have it all wrong!have it all wrong!

Unfortunately, I’d say >90% 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 don’t 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



Device-Level Requirements for Reversible Device-Level Requirements for Reversible ComputingComputing

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 it’s still a requirement (to a lesser extent) in logic as well

Low energy coefficient cEt = Ediss·ttr (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

Plenty of Room forPlenty of Room forDevice ImprovementDevice Improvement 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 today’s CMOS! Ultimate limits are unclear.

.18µm CMOS.18µm

2LAL

k(300 K) ln 2

Variousreversibledevice proposals

Power per device, vs. frequency

One Optimistic ScenarioOne Optimistic ScenarioA Potential Scenario for CMOS vs. Reversible Raw Affordable Chip Performance

1.00E+17

1.00E+18

1.00E+19

1.00E+20

1.00E+21

1.00E+22

1.00E+23

2004 2006 2008 2010 2012 2014 2016 2018 2020

Year

Dev

ice-

op

s/se

con

d p

er a

ffo

rdab

le 1

00W

ch

ip

CMOS

Reversible

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!)

40 layers, ea. w.8 billion activedevices,freq. 180 GHz,0.4 kT dissip.per device-op

Microsoft Excel Worksheete.g. 1 billion devices actively switching at

3.3 GHz, ~7,000 kT dissip. per device-op



A Call to ActionA 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 nation’s (and the world’s) top

physicists and computer scientists must be recruited, to tackle the great “Reversible Computing Challenge.”

Urgently needed: A major new funding program;a “Manhattan Project” for energy-efficient computing! Mission: Demonstrate computing beyond the von Neumann-

Landauer limit in a practical, scalable machine! Or, if it really can’t be done, for some subtle reason, find a completely

rock-solid proof from fundamental physics showing why.



Efficiency in General, Efficiency in General, and Energy Efficiencyand Energy Efficiency

The efficiency η of any process is: η = P/C Where P = Amount of some valued product produced and C = Amount of some costly resources consumed

In energy efficiency ηe, the cost C measures energy. We can talk about the energy efficiency of:

A heat engine: ηhe = W/Q, where: W = work energy output, Q = heat energy input

An energy recovering process : ηer = Eend/Estart, where: Eend = available energy at end of process, Estart = energy input at start of process

A computer: ηec = Nops/Econs, where: Nops = # useful operations performed Econs = free-energy consumed

FAMU-FSU College of Engineering 6/19/06M. Frank, NSF/CISE/CCF job talk1 Addressing the Funding Gap...

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Transcript of FAMU-FSU College of Engineering 6/19/06M. Frank, NSF/CISE/CCF job talk1 Addressing the Funding Gap...