Addressing the Funding Gap in Energy-Efficient

Computing Research Overview and Program

Management Philosophy

- By Michael P. FrankPresented to the National

Science FoundationDirectorate for Computer

Information Science EngineeringComputer

Communication Foundations (CCF) DivisionMonday,

July 10, 2006

Overview of Talk

- Motivation
- The Looming Energy Efficiency Crisis in Computing
- and the related Funding Gap between government

industry - The Science
- Why something called Reversible Computing is

really Our Only Hope for solving the problem - And why we need to start major research on it

now! - Why Im Here
- Convey my vision of CCF, the EMT program and how

the field of Reversible Computing fits into them - Ideas on how I would help run the EMT program

Motivation

- The Coming Crisis in Computer Energy Efficiency

Major Motivation of my WorkThe Energy

Efficiency Crisis

- The bulk of past improvements in practical

computer performance have been fundamentally

enabled by steady improvements in the energy

efficiency of computation - Defined as the number of useful computational

operations performed per unit of available energy

dissipated into the form of waste heat - Unfortunately, an end to the past trend of steady

energy efficiency improvements is now clearly

within sight - Designs at many levels (devices, circuits,

architectures, algorithms) for conventional

computing are rapidly converging towards optimal

design-point asymptotes, within a few-decade

time-frame - Beyond which substantial further progress will

not be possible, at least not within the

conventional classical, irreversible computing

paradigm - To circumvent the crisis, a radical paradigm

shift in our models and structures for

computation is required! - I will show why reversible computing will be an

essential part of this.

Computings Rapid Climb

- The raw performance efficiency characteristics

of our information processing technologies

(computing, storage, communication) have been

improving at a steady, exponentially increasing

rate over time, for at least the past 50 years - Due to Moores Law (integration scale of

electronics doubles every 1-2 years) and related

technology trends - Performance trends also span multiple pre-IC

technologies (vacuum tubes, relays, etc.) going

back 100 years or more - Each generation of performance improvements has

reliably led to significant new

information-processing applications becoming

practicable

Substantial Societal Impact

- Economic measures of the nations ( worlds)

economy, such as GDP, per-capita income, and

standard of living have also improved

exponentially (although at slower rates) over

this same period - Its clear that a substantial portion of these

gains was made possible by the introduction of

new IT applications, itself made possible by raw

technology improvements - Nearly every major industry today has relied on

digital/ electronic technologies for a

substantial portion of the productivity gains it

has made over the last few decades - Effected either directly, or indirectly through

its suppliers

These historical observations raise an important

concern

- We can arguably expect that the future rate of

growth of the entire world economy will

substantially depend on future trends in

information technology efficiency - I.e., will our raw technologycapabilities

flatten out,continue improvingsteadily, or

accelerate even faster than before?

logefficiency

now

decade

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 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

Trend of Minimum Transistor Switching Energy

Based on Data from International Technology

Roadmaps for Semiconductors

Node numbers(nm DRAM hp)

Historical trendline

Conservative industry targets

CV2/2 gate energy, Joules

An Urgent Scientific Need

- Given the above considerations, I would say that

one of the most important basic research issues

that our society needs the field of computer

science engineering to address is to find a

definitive answer to the following question - Can the introduction of new alternative,

unconventional computing paradigms (such as

reversible, quantum, and bio-inspired computing)

realistically prevent or forestall the

flattening of the information technology curve? - And if so, how exactly can this work?
- My vision is that answering this question should

be a primary scientific mission of the EMT

program. - Although other applications are also important

The Science

- Why Reversible Computing is Our Last, Great

Hope for Continuing to Improve Computing

Indefinitely

The von Neumann-Landauer (VNL) Bound

- Physical theorem To lose, obliviously erase, or

otherwise irreversibly forget 1 bits worth of

known information involves/requires the eventual

dissipation of at least kBT ln 2 amount of free

energy to heat in an external environment at some

temperature T. - kB here is Boltzmanns constant, 1.3810-23 J/K

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

clarified and proven by Rolf Landauer, 1961.

A simple proof of the VNL bound

- Heres a simple proof, from basic thermodynamic

facts known for gt100 years! - If known information becomes unknown, this is (by

defn) an increase of entropy. - Because entropy is simply unknown physical

information. - And, all information that is accessible to us is

physical information anyway. - Standard units of information and entropy are

simply logarithmic units - 1 bit log 2 ?b.logb2 (indefinite logarithm

object), Boltzmanns constant kB log e - Therefore, in units of Boltzmanns constant, 1

bit kB(log 2/log e) kB ln 2 - Thus, the loss (forgetting) of 1 bit is, by

definition, the very same thing as an increase of

entropy by the amount kB ln 2. - Once entropy is created, it can never be

destroyed (2nd law of thermodynamics) - This follows from the micro-scale reversibility

of basic laws of (today quantum) mechanics - As entropy builds up in a system, its temperature

rises. - To operate sustainably without eventual meltdown,

- The entropy generated must be expelled to an

external environment. - To add entropy S to an environment at temperature

T requires adding energy E ST to that

environment - this is the very definition of

thermodynamic temperature! - Thus, to forget a bit (i.e., permanently expel it

into the environment) requires that we must

eventually permanently commit energy kBT ln 2 to

the environment (as heat).

An Essential Element of Future Paradigms

Reversible Computing

- Basic idea (R. Landauer, 1961 C. Bennett,

1973) - Fundamental physics suggests that in principle

there is no limit to the energy efficiency of

computing technologies, although this is true

only to the extent that we avoid performing

irreversible operations that discard information

during the computing process - But, it seems that with sufficient engineering

effort, we can in principle approach, as closely

as we care to, the limit of a reversible computer

that discards no information and dissipates no

energy - Our practical aim is not zero energy, just

continued steady reductions! - Present status of reversible computing
- Potential advantages/tradeoffs are reasonably

well understood - Models early prototypes exist, but no practical

systems yet - Of interest to other clusters Implementing this

notion would eventually impact computer

engineering CS at all levels! - From low-level physical device requirements up

through circuit design, theory, architecture,

languages, algorithms

Irreversible vs. Reversible Digital Operations

- A typical irreversible digital operation
- Regardless of the previous digital contents x of

some circuit node or memory cell, destructively

overwrite it with a given new value y. - A closely corresponding, but reversible

operation - Reversibly transform the old physical state

representing x in place to a new state the new

value y. - The semantic difference is that the 2nd op can

only be done if the old value x is known - This means, it can be reconstructed based on the

new value y together with other available

information. - This restricts the kinds of replacements that can

be done reversibly - e.g., cant replace two bits a,b with the product

ab and 1 other bit

x

y

bit bucket

y

x

Simple Electronic Implementations

- Irreversible CLEAR (set to 0) operation
- Without knowing if there is charge on node N,

connect it to ground (logic 0 reference level) - The stored information is lost and the entire

associated node energy E is dissipated to heat!

- Reversible CLEAR(change from 1 to 0)
- Given that N contains a 1, we connect it to a

source that goes from 1 to 0 over time t gt tc - Only a fraction tc/t of the node energy E is

dissipated, - tc 2RC is a time constant
- R resistance of path
- C capacitance of node

N

N

Switch open

N

Switch closed

1

Variablesource

0

Node is charged upwith an amount E

ofelectrostaticenergy

R

Node dischargessuddenly,all info energy

arefully lost

t

C

Charge Q (2EC)1/2 flows out in a controlled way

over time t, dissipation Ediss I2Rt Q2R/t

E(2RC/t)

(Adiabatic charge transfer)

Simulation Results (Cadence/Spectre)

- Graph shows power dissipation vs. frequency
- in 8-stage shift register.
- At moderate frequencies (1 MHz),
- Reversible uses lt 1/100th the power of

irreversible! - At ultra-low power (1 pW/transistor)
- Reversible is 100 faster than irreversible!
- Minimum energy dissip. per nFET is lt 1 eV!
- 500 lower than best irreversible!
- 500 higher computational energy efficiency!
- Energy transferred is still 10 fJ (100 keV)
- So, energy recovery efficiency is 99.999!
- Not including losses in power supply, though

2LAL Two-level adiabatic logic (invented at UF,

00)

1 nJ

100 pJ

Standard CMOS

10 aJ

10 pJ

1 aJ

1 pJ

Energy dissipated per nFET per cycle

1 eV

100 fJ

2V

100 zJ

2LAL 1.8-2V

1V

10 fJ

10 zJ

0.5V

0.25V

kT ln 2

1 fJ

1 zJ

100 aJ

100 yJ

Reversible and/or Adiabatic VLSI Chips Designed

_at_ MIT, 1996-1999

By EECS grad students Josie Ammer, Mike Frank,

Nicole Love, Scott Rixner,and Carlin Vieri under

CS/AI lab members Tom Knight and Norm Margolus.

Some Important Results in Reversible Computing

So Far

- Landauer (IBM) 1961
- The von Neumann limit of kT ln 2 energy

dissipation per bit operation only holds for

irreversible operations. - Lecerf 1963, Bennett (IBM) 1973
- Computers that use only reversible operations are

still Turing universal. - Fredkin Toffoli (MIT), 1980
- Reversible computers can be implemented in an

idealized classical physical model. - Feynman (CalTech), 1982
- Reversible computers can be implemented in a

simple quantum physical model. - This paper eventually spawned the field of

quantum computing - Younis Knight (MIT), 1993
- Pipelined, sequential logic circuits can be

implemented in fully-reversible CMOS. - This paper helped to spawn the field of adiabatic

circuits - MIT Pendulum Project (Ammer, Frank, Knight, Love,

Margolus, Rixner, Vieri), 1994-1999 - Designed implemented fully reversible

programmable circuits, general-purpose RISC

architectures, high-level programming languages,

and algorithms for a wide variety of classical CS

problems - Frank (MIT) 1997-1999
- When physical constraints are accounted for,

reversible computers offer asymptotically lower

energy, cost, and time complexity for broad

classes of problems than conventional machines. - Frank (UF) 2000-2002
- The advantages of reversible computing over

conventional computing increase as small

polynomials of the underlying technology

characteristics The trends show reversible

winning within decades for machines at usual

scales

Important Open Research Challenges in Reversible

Computing

- Fundamental research on practicability of

reversible computing - (Physics) Can we invent post-transistor devices

with lower leakage and energy coefficients? - This research requires cross-disciplinary

collaboration with physicists - (Engineering) Can we tailor physical mechanisms

to precisely execute complex trajectories

(computations) with high energy-recovery

efficiency? - E.g. efficient resonators and power-clock

distribution systems driving adiabatic logic.

Collaboration with extremely skilled EEs is

needed - (Structures) Can we design mostly-reversible

architectures with low overhead for practical

special-purpose applications, at least? - Existing general-purpose reversible architectures

are highly suboptimal - (Theory) Can we reversibly emulate general

irreversible algorithms with less space-time

complexity overhead than presently known? - Oracle-based results suggest not, but more work

is needed

The Funding Gap inEnergy-Efficient Computing

- As a proposal writer, Ive found that reversible

computing falls into a rather awkward, in-between

position - Because it aims to help a broad range of

practical applications, and is well-motivated by

basic physics, many scientists who evaluate RC

proposals say it seems too practical to receive

basic research funding, they expect its

development should be funded by industry. - Yet, because RC is high-risk, very disruptive,

and probably will take much longer than

industrys traditional 10-year lab-to-fab time

lag to develop and broadly adopt, industry has

largely ignored it, in favor of more short-term

approaches to save energy - The major risk that society faces in allowing

this funding gap to persist is that if industry

steps in too late, then workable, practical

implementations of RC might not be ready in time

to prevent performance growth from stalling - If there is even a brief stall, the loss of

momentum could breed pessimism and choke off

industrys will to continue innovating

Why Im Here

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

into it

Areas Covered by CCF

- Emerging Models and Technologies (EMT)
- Paradigms Nanocomputing, quantum computing,

biologically inspired computing - I would add reversible computing to this list
- Founds. of Comp. Procs. Artifs. (FCPA)
- Structures Programming languages, computer

architecture, VLSI design - Theoretical Foundations (TF)
- Theory Models of computation, complexity,

parallelism, algorithms, information theory

Some Highlights of My Related Educational

Background

- Early exposure to nanotech/nanocomputing concepts
- Nanotechnology course, K. Eric Drexler, Stanford,

1988 - Solid general background in CS theory AI
- BS in Symbolic Systems, Stanford, 1991
- MS in EECS on Decision-Theoretic techniques in

AI, MIT, 1994 - Ph.D. proposal on DNA-based computing
- MIT Lab for CS, 94-95
- Fairly early exposure to Quantum Computing
- Reviewed the field for MIT EECS Ph.D. area exam,

1995 - Ph.D. minor in conventional CMOS VLSI design
- Designed had fabbed several chips, for courses

Ph.D. work - Ph.D. work on Reversible Computing
- Included development of nanocomputing models,

complexity theory, architectures, programming

languages, VLSI design

What I See As Some General Research Questions

Behind EMT

- What are the fundamental physical limits of

present future information processing

technologies? - As opposed to the more abstract, algorithmic

kinds of limits addressed by traditional

theoretical CS - What fundamental changes to our underlying

models/paradigms of computation may we need in

order to fully harness emerging technologies? - New models based on physics (or chemistry,

biology?) - How can practical considerations help to guide

our exploration of the emerging technology

concepts? - E.g., concerns with (at least estimates of)

real-world cost, performance, energy efficiency,

reliability, ease of use

Some Cross-Cutting Questions to other areas of

CCF

- Cross-cutting to FCPA cluster
- What would the emergence of new computing

paradigms require in terms of new architectures,

programming languages, HW design tools? - Cross-cutting to TF cluster
- What impacts do emerging technologies have on

theoretical CS areas such as models of

computation, complexity theory, algorithm design,

and parallel computing?

What are the Fundamental Physical Limits of

Computing?

- Fundamental laws of physics impose a variety of

universal limits that hold true in all physically

possible information processing technologies - Thermodynamic von Neumann/Landauer (VNL) lower

bound of kT ln 2 (18 meV at room temperature) on

energy dissipated per known bit that is discarded

into a temperature-T environment. - However, this one could be avoided via reversible

computing - Quantum performance limit (Margolus-Levitin

bound) of at most a rate 2E/h (hPlancks

constant) of useful bit operations in any

device with an active energy of E. - This limit applies even to reversible quantum

computers! - There are also fundamental physical limits on

information density and bandwidth, but I wont

get into those here

What Changes to Our Models/Paradigms are Needed?

- Two of the most important new paradigms
- Reversible computing teaches us that overcoming

the energy-efficiency crisis will eventually

require an emphasis on reversible operations,

impacting increasingly higher levels throughout

computing. - Quantum computing teaches us that the fastest

known algorithms for certain classes of problems

require machines that provide the ability to

perform uniquely quantum operations.

New Paradigms for Computing

- Reversible computing aims to directly circumvent

the energy efficiency problem through the use of

energy-conserving physical mechanisms for

information processing - Quantum computing aims for dramatic algorithmic

improvements for some types of problems, using

shortcuts through state space made possible by

nonclassical operations - Bio-inspired computing broadly includes
- In vivo biological computing, e.g., bacteria

genetically engineered to incorporate custom gene

expression regulation networks - In vitro biochemistry-based computing such as DNA

computing and related approaches - In silico but still biologically-inspired

techniques such as digital analog neural

networks, other analog approaches, neuromorphic

computing, etc

New Paradigms in Relation to What I see as EMTs

Mission

- Bio-inspired computing is interesting, but

generally incapable of superseding the limits of

conventional technology by very much - All realistic bio-inspired approaches could be

simulated by conventional parallel digital

machines with (at most) modest constant-factor

overheads - The motivation for bio-inspired computing must

come from other directions - Quantum computing is nice if it can be made to

work, but as far as we know, it is limited in its

applicability to relatively narrow classes of

problems (e.g., hidden subgroup, modest gains for

search) - Its potential economic impact is therefore only a

small fraction of that for all leading-edge

computing in general - Research that aims to broaden its applicability

is potentially worthwhile - Reversible computing is the only unconventional

paradigm that might possibly break down the

roadblocks to indefinite future improvement of

computer efficiency and practical performance in

general applications - Its future economic value is thus potentially

unlimited - However, it is difficult to do, and still in its

infancy! Much research is needed.

Some Other Motivations for Paradigms Covered by

EMT

- Bio-inspired computing
- In vivo computing Self-reproducing,

self-organizing microbial systems for various

clinical or industrial applications - In vitro computing Self-assembly of

nanostructures - Neural networks Applications in machine learning
- Analog electronics Low-power signal processing
- Quantum computing
- Fast factoring etc. for cryptanalysis of PK

cryptosystems - Strong information security via quantum

cryptography - Fast, flexible, accurate simulation of quantum

physical systems - Reversible computing
- Reversible logic is already used in quantum

computing, and has a few possible applications in

other areas of CS - Security auditable/verifiable computation,

resilient systems - Transaction rollback for concurrent systems
- May conceivably provide useful angles for

tackling complexity-theory questions - e.g., FACTORING?P iff ? a poly-time zero-garbage

reversible alg. to multiply primes

Some Important Research Challenges in Quantum

Computing

- Important experimental physics challenges
- Develop new experimental setups for prototype

quantum computers that can effectively suppress

decoherence to the threshold for fault-tolerance - To enable more rapid improvement of machine sizes
- Develop effective physical architectures for

efficient qubit transfer execution of parallel

quantum circuits - Important theory challenges
- Better characterize the limits of applicability

of quantum algorithms - Find major new categories of applications beyond

the scope of the standard hidden subgroup /

unstructured search algorithms - Resolve major open issues in quantum complexity

theory - Comparisons between BQP vs. BPP and NP, etc.

Program Administration Ideas

- My personal program management philosophy
- Hands-on leadership, guiding steering the

work of proposers reviewers based on my vision

and understanding of the programs mission and

the scientific needs of the fields that it

touches on - Clarify the vision and goals of the funding

program up-front with a technical white paper

surveying important open scientific issues - Include motivation for and summaries of important

open research problems, with references to the

literature - Encourage proposal writers to address the listed

issues, or else to thoroughly motivate their own

alternative directions - Proactively seek out researchers whose

background, skills, and research interests seem

to mesh well with the clusters mission and

vision - and encourage them to submit proposals to the

program - Encourage review panel members to carefully

consider the quality thoroughness of the

motivation section when evaluating the scientific

merit of proposals - IMHO, too much of todays research is not

sufficiently well-motivated

Educational Component

- Strongly encourage proposers to include

educational activities in their proposals,

including - Organizing of conferences
- Writing of technical books textbooks
- Writing of introductory books for popular

audiences - Even encourage submission of proposals for

activity that is primarily educational in nature - There is an education gap in the areas I

discussed also - Especially in reversible computing, which is

still little known - Emphasize the need for educational materials that

have a strong interdisciplinary perspective - E.g., integrating CS, EE, physics issues

Conclusion

- Among the various unconventional computing

technologies, there are strong reasons to believe

that reversible computing has the greatest

potential to make an enormous, vital, broad, and

timely economic impact in coming decades - Yet, compared to areas such as DNA, quantum, nano

and bacterial computing, it has received by far

the least attention and funding! - One of my main motivations for working in

reversible computing has been to correct the

imbalance between the underlying importance of

and popular attention to this field - However, my influence as a lone researcher in

the trenches is limited No programs support

this presently unfashionable field - I hope in my position at EMT to help to finally

bring some much-needed funding and attention to

this orphaned area, and help guide research in

new, productive directions - While continuing support for well-motivated

projects in other areas

finis

- End of Presentation Extra Slides Follow

Goals of Presentation

- Convey my vision for research education

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

Structure of Talk

- Briefly introduce what I believe are some

important scientific questions that CCF can work

to address - Both within EMT, and potentially cross-cutting to

other clusters within CCF, even to other

directorates - Engineering, physical sciences
- Summarize some of my own past research that

relates to these questions - Work done at MIT, Univ. of Florida, and Florida

State - List some related research challenges
- Present some ideas/strategies for administration

of CCF programs so as to facilitate scientific

progress on these issues

Everyone Has It All Wrong!

- As the talk proceeds,
- Ill explain (in the proud MIT tradition) why

most of the rest of the world is thinking about

the future of computing in a completely

wrong-headed way. - In particular,
- The Low-Power Logic Circuit Designers have it all

wrong! - The Semiconductor Process Engineers have it all

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

The von Neumann-Landauer (VNL) principle

- John von Neumann, 1949
- Claim The minimum energy dissipated per

elementary (binary) act of information is kT ln

2. - No published proof exists only a 2nd-hand

account of a lecture - Rolf Landauer (IBM), 1961
- Logically irreversible (many-to-one) bit

operations must dissipate at least kT ln 2

energy. - Paper anticipated but didnt fully appreciate

reversible computing - One proper (i.e. correct) statement of the

principle - The oblivious erasure of a known logical bit

generates at least k ln 2 amount of new entropy. - Releasing into environment at T requires kT ln 2

heat emission.

Proof of the VNL Principle

- The principle is occasionally questioned, but
- Its truth follows absolutely rigorously (and even

trivially!) from rock-solid principles of

fundamental physics! - (Micro-)reversibility of fundamental physics

implies - Information (at the microscale) is conserved
- I.e., physical information cannot be created or

destroyed - only transformed via reversible, deterministic

processes - Thus, when a known bit is erased (lost,

forgotten) it must really still be preserved

somewhere in the microstate! - But, since its value has become unknown, it has

become entropy - Entropy is just unknown/incompressible information

Types of Dynamical Processes

- These animations illustrate how states transform

in their configuration space, in - A nondeterministic process
- One-to-many transformations
- An irreversible process
- Many-to-one transformations
- Nondeterministic and irreversible
- Deterministic and reversible
- One-to-one transformations only!

WE ARE HERE

Physics is Reversible!

- Despite all of the empirical phenomenology

relating to macro-scale irreversibility, chaos,

and nondeterministic quantum events, - Our most fundamental and thoroughly-tested modern

models of physics (e.g. the Standard Model) are,

at bottom, deterministic reversible! - All of the observed nondeterministic and

irreversible phenomena can still be explained

within such models, as emergent effects. - Although classical General Relativity is argued

by some researchers to have certain irreversible

aspects, - The general consensus seems to be that well

eventually find that the correct theory of

quantum gravity will be reversible.

Reversible/Deterministic Physics is Consistent

with Observations

- Apparent quantum nondeterminism can validly be

understood as an emergent phenomenon, an expected

practical result of permanent wavefunction

splitting - As illustrated e.g. in the many worlds and

decoherent histories pictures - Even if a quantum wavefunction does not split

permanently, its evolution in a large system can

quickly become much too complex to track within

our models - Thus we resort to using reduced density

matrices, which discard some knowledge - The above effects, plus imprecision in our

knowledge of fundamental constants, result in

some practical unpredictability even for

microscale systems - Thus entropy, for all practical purposes, tends

to increase towards its maximum - Chaos (macro-scale nondeterminism) occurs when

entropy at the microscale infects our ability to

forecast the long-term evolution of macroscopic

variables - A necessary consequence of the computation-univers

ality of physics? - Meanwhile, averaging of many high-entropy

microscopic details results in a smoothing

effect that leads to irreversible evolution of

macro-variables.

Reversible Computing

- Wed like to design mechanisms that compute while

producing as little entropy as possible - In order to minimize consumption of free energy /

emission of heat to the environment - Losing known information necessarily results in a

minimum k ln 2 entropy increase per bit lost, so - Lets consider what we can do using logically

reversible (one-to-one) operations that dont

lose information. - Such operations are still computationally

universal! - Lecerf (1963), Bennett (1973)

Conventional Gate Operations are Irreversible

(even NOT!)

- Consider a computer engineers (i.e., real

world!) Boolean NOT gate (a.k.a. logical

inverter) - Specified function Destructively overwrite

output nodes value with the logical complement

of the input!

Hardwarediagram

Space-time logic networkdiagram (not the same

thing!!)

New in

in

Oldin

Twodifferentphysicallogicnodes

Inverteroperation

Invertergate

Oldout

New out

out

time

In-Place NOT (Reversible)

- Computer scientists (i.e., somewhat

fictionalized!) in-place logical NOT operation - Specified operation Replace a given logic

signal with its logical complement. - People occasionally confuse the irreversible

inverter operation with a reversible in-place NOT

operation - The same icon is sometimes used in spacetime

diagrams

time

time

in

out

old bit

new bit

In-Place Controlled-NOT (cNOT)

- Specified function Perform an in-place NOT on

the 2nd bit if and only if the 1st bit is a 1. - Equiv., replace 2nd bit with XOR of 1st 2nd bits

Before Before After After

C D C D

0 0 0 0

0 1 0 1

1 0 1 1

1 1 1 0

Transitiontable

control

old data

new data

time

Early Universal Reversible Gates

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

A

B

C

A

B

C

The Adiabatic Principle

- Applied physicists know that a wide class of

physical transformations can be done

adiabatically - From Greek adiabatos, It shall not be passed

through - Used to mean, no passage of heat through an

interface separating subsystems at different

temperatures - Newer, more general meaning No increase of

entropy - Of course, exactly zero entropy increase isnt

practically doable - In practice, adiabatic is used to mean that the

entropy generation scales down proportionally as

the process takes place more gradually. - The general validity of this 1/t scaling relation

is enshrined in the famous adiabatic theorem of

quantum mechanics.

Adiabatic Charge Transfer

Q

- Consider passing a total quantity of charge Q

through a resistive element of resistance R over

time t via a constant current, I Q/t. - The power dissipation (rate of energy diss.)

during such a process is P IV, where V IR is

the voltage drop across the resistor. - The total energy dissipated over time t is

therefore E Pt IVt I2Rt (Q/t)2Rt

Q2R/t. - Note the inverse scaling with the time t.
- In adiabatic logic circuits, the resistive

element is a switch. - The switch state can be changed by other

adiabatic charge transfers. - In simple FET-type switches, the constant factor

(energy coefficient) Q2R appears to be subject

to some fundamental quantum lower bounds. - However, these are still rather far away from

being reached.

R

The Low-Power Design community has it all wrong!

- Even (most of) the ones who know about adiabatics

and even many who have done extensive amounts of

research on adiabatic circuits still arent doing

it right! - Watch out! 99 of the so-called adiabatic

circuit designs published in the low-power design

literature arent truly adiabatic, for one reason

or another! - As a result, most published results (and even

review articles!) dramatically understate the

energy efficiency gains that can actually be

achieved with correct adiabatic design. - Which has resulted in (IMHO) too little serious

attention having been paid to adiabatic

techniques.

Circuit Rules for True Adiabatic Switching

- Avoid passing current through diodes!
- Crossing the diode drop leads to irreducible

dissipation. - Follow a dry switching discipline (in the relay

lingo) - Never turn on a transistor when VDS ? 0.
- Never turn off a transistor when IDS ? 0.
- Together these rules imply
- The logic design must be logically reversible
- There is no way to erase information under these

rules! - Transitions must be driven by a quasi-trapezoidal

waveform - It must be generated resonantly, with high Q
- Of course, leakage power must also be kept

manageable. - Because of this, the optimal design point will

not necessarily use the smallest devices that can

ever be manufactured! - Since the smallest devices may have insoluble

problems with leakage.

Importantbut oftenneglected!

Conditionally Reversible Gates

- Avoiding VNL actually only requires that the

operation be one-to-one on the subset of states

actually encountered in a given system - This allows us to design with gates that do

conditionally reversible operations - That is, they are reversible if certain

preconditions are met - Such gates can be built easily using ordinary

switches! - Example cSET (controlled-SET) and cCLR

(controlled-CLR) operations can be implemented

with a single digital switch (e.g. a CMOS

transmission gate), with operation timing

controlled by an externally-supplied driving

signal - These operations are conditionally reversible, if

preconditions are met

Hardwareschematic

Hardwareicon

Space-time logic diagram

in

in

in

drive

drive

newout in

oldout 0

finalout 0

0?1

1?0

out

out

Reversible OR (rOR) from cSET

- Semantics rOR(a,b)if ab, c1.
- Set c1, if either a or b is 1.
- Reversible if initially ab ? c.
- Two parallel cSETs simultaneouslydriving a

shared output busimplements the rOR operation! - This is a type of gate composition that was not

traditionally considered. - Similarly, one can do rAND, and reversible

versions of all Boolean operations. - Logic synthesis with theseis extremely

straightforward

Hardware diagram

a

c

b

Spacetime diagram

a

a

a OR b

0

c

c

b

b

Semiconductor Process Engineers have it all wrong!

- Everybody still thinks that smaller FETs

operating at lower voltages will forever be the

way to obtain ever more energy-efficient and more

cost-efficient designs. - But if correct adiabatic design techniques are

included in our toolbox, this is simply not true! - With good energy recovery, higher switching

voltages (requiring somewhat larger devices)

enable strictly greater overall energy

efficiency! (and thus lower energy cost!) - This is due to the suppression of FET leakage

currents exponentially with Vq/kT. - The hardware cost-performance overheads of this

approach only grow polylogarithmically with the

energy efficiency gains - Over time, we can expect the overheads will be

overtaken by competitively-driven per-device

manufacturing cost reductions - If devices better than FETs arent found,
- then I predict an eventual bounce in device

sizes

The Need for Ballistic Processes

- In order to achieve low overall entropy

generation in a complete system, - Not only must the logic transitions themselves

take place in an adiabatic fashion, - but also the components that drive and control

the signal levels and timing of logic transitions

(power clocks) must proceed reversibly along

the desired trajectory. - Thus, we require a ballistic driving mechanism
- One that proceeds under its own momentum along

a desired trajectory with relatively little

entropy increase. - Many concepts for such mechanisms have been

proposed, but - Designing a sufficiently high-quality power-clock

mechanism remains the major unsolved problem of

reversible computing

Fredkin and Toffolis (1980) Billiard-Ball Model

- 1st conceptual model of a ballistic physical

computing process - Perfectly rigid billiard balls bounce off walls

each other in digitally-precise trajectories

- Shown to be capable of asymptotically efficient

simulations of arbitrary reversible circuits in

2D (extensible to 3D also) - Its idealized it would be chaotically unstable

in practice - The addition of appropriate constraining

mechanisms to prevent the balls from going off

track or out of sync is viewed as a later step - Zurek argued that analogous quantum processes can

avoid the chaos

Requirements for Energy-Recovering Clock/Power

Supplies

- All of the known reversible computing schemes

require the presence of a periodic and globally

distributed signal that synchronizes and drives

adiabatic transitions in the logic. - For good system-level energy efficiency, this

signal must oscillate resonantly and

near-ballistically, with a high effective quality

factor. - Several factors make the design of a resonant

clock distributor that has satisfactorily high

efficiency quite difficult - Any uncompensated back-action of logic on

resonator - In some resonators, Q factor may scale

unfavorably with size - Excess stored energy in resonator may hurt the

effective quality factor - Theres no reason to think that its impossible

to do it - But it is definitely a nontrivial hurdle, that we

reversible computing researchers need to face up

to, pretty urgently - If we hope to make reversible computing practical

in time to avoid an extended period of stagnation

in computer performance growth.

MEMS Resonator Concept

Arm anchored to nodal points of fixed-fixed beam

flexures,located a little ways away, in both

directions (for symmetry)

z

y

Phase 180 electrode

Phase 0 electrode

Repeatinterdigitatedstructurearbitrarily

manytimes along y axis,all anchored to the

same flexure

x

C(?)

C(?)

0

360

0

360

?

?

(PATENT PENDING, UNIVERSITY OF FLORIDA)

MEMS Quasi-Trapezoidal Resonator 1st Fabbed

Prototype

(Funding source SRC CSR program)

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

Primaryflexure(fin)

Sensecomb

Drive comb

(PATENT PENDING, UNIVERSITY OF FLORIDA)

Would a Ballistic Computer be a Perpetual Motion

Machine?

- Short answer No, not quite!
- Hey, give us some credit here!
- Were hard-core thermodynamics geeks, we know

better than that! - Two traditional (and impossible!) kinds of

perpetual motion machines - 1st kind Increases total energy - Violates 1st

law of thermo. (energy conservation) - 2nd kind Reduces total entropy - Violates 2nd

law of thermo. (entropy non-decrease) - Another kind that might be possible in an ideal

world, but not in practice - 3rd kind Produces exactly 0 increase in

entropy! - Requires perfect knowledge of physical constants,

perfect isolation of system from environment,

complete tracking of systems global

wavefunction, no decoherence, etc. - What were more realistically trying to build in

reversible computing is none of the above, but

only the more modest goal of a For-a-long-time

Motion Machine - I.e., one that just produces as close to zero

entropy (per op) as we can possibly achieve! - It would coast along for a while, but without

energy input, it would eventually halt - Such a coasting machine can perform no net

mechanical work in a complete cycle, - But it can potentially do a substantial amount of

useful computational work!

Some Results on Scalability of Reversible

Computers

- In a realistic physics-based model of computation

that accounts for thermodynamic issues - When leakage is negligible and heat flux density

is bounded, - Adiabatic machines asymptotically outperform

irreversible machines (even per unit cost!) as

problem sizes machine sizes are scaled up - But, the absolute speedup when total system power

is unrestricted grows only as a small polynomial

with the machine size - E.g., exponents of 1/36 or 1/18, depending on

problem class - The speedup per unit surface area or

(equivalently) per unit power dissipation grows

at a somewhat faster (but still gradual) rate - E.g., with the 1/6 power of machine size
- Even when leakage is non-negligible,
- Adiabatic machines can still attain

constant-factor (i.e., problem-size-independent)

energy savings ( speedups at fixed power) that

scale as moderate polynomials of the device

characteristics - E.g., roughly with the transistor on-off ratio to

at least the 0.39 power - Cost overheads from RC in these scenarios also

grow, somewhat faster - But, we can hope that device costs will continue

to decline over time

Bennetts 1989 Algorithmfor Worst-Case

Reversiblization

k 3n 2

k 2n 3

Worst-Case Energy/Cost Tradeoff(Optimized

Bennett-89 Variant)

cost ? energy ?1.59

Spacetime cost blowup factor

Energy savings factor

k

n

(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

Device-Level Requirements for Reversible Computing

- A good reversible digital bit-device technology

should have - Low amortized manufacturing cost per device, d
- Important for good overall (system-level)

cost-efficiency - Low per-device level of static standby power

dissipation Psb due to energy leakage,

thermally-induced errors, etc. - This is required for energy-efficient storage

devices, especially - but its still a requirement (to a lesser extent)

in logic as well - Low energy coefficient cEt Edissttr (energy

dissipated per operation, times transition time)

for adiabatic transitions between digital states. - This is required in order to maintain a high

operating frequency simultaneously with a high

level of computational energy efficiency. - And thus maintain good hardware efficiency (thus

good cost-performance) - High maximum available transition frequency fmax.
- This is especially important for applications in

which the latency from inherently serial

computing threads dominates total operating costs

Plenty of Room forDevice Improvement

Power per device, vs. frequency

- Recall, irreversible device technology has at

most 3-4 orders of magnitude of

power-performance improvements remaining. - And then, the firm kT ln 2 (VNL) limit is

encountered. - But, a wide variety of proposed reversible device

technologies have been analyzed by physicists. - With preliminary estimates of theoretical

power-performance up to 10-12 orders of magnitude

better than todays CMOS! - Ultimate limits are unclear.

.18µm CMOS

.18µm 2LAL

k(300 K) ln 2

Variousreversibledevice proposals

One Optimistic Scenario

40 layers, ea. w.8 billion activedevices,freq.

180 GHz,0.4 kT dissip.per device-op

e.g. 1 billion devices actively switching at3.3

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

Note that by 2020, there could be a factor of

20,000 difference in rawperformance per 100W

package. (E.g., a 100 overhead factor from

reversible design could be absorbed while still

showing a 200 boost in performance!)

A Call to Action

- The world of computing is threatened by permanent

raw performance-per-power stagnation in 1-2

decades - We really should try hard to avoid this, if at

all possible! - A wide variety of very important applications

will be impacted. - Many more of the nations (and the worlds) top

physicists and computer scientists must be

recruited, - to tackle the great Reversible Computing

Challenge. - Urgently needed A major new fundin