IEEE Spectrum September, 2017 - 37

Meanwhile, other researchers had been exploring alter‑
native approaches to implementing reversible computing
that were not based on semiconductor electronics at all. In
the early 1990s, nanotechnology visionary K. Eric Drexler
produced detailed designs for reversible nanomechanical
logic devices made from diamond‑like materials. Over the
decades, Russian and Japanese researchers had been devel‑
oping reversible superconducting electronic devices, such as
the similarly named (but distinct) parametric quantron and
quantum flux parametron. And a group at the University of
Notre Dame was studying how to use interacting single elec‑
trons in arrays of quantum dots. To those of us who were
working on reversible computing in the 1990s, it seemed
that, based on the wide range of possible hardware that had
already been proposed, some kind of practical reversible
computing technology might not be very far away.
Alas, the idea was still ahead of its time. Conventional semi‑
conductor technology improved rapidly through the 1990s and
early 2000s, and so the field of reversible computing mostly
languished. Nevertheless, some progress was made. For
example, in 2004 Krishna Natarajan (a student I was advising
at the University of Florida) and I showed in detailed simula‑
tions that a new and simplified family of circuits for reversible
computing called two‑level adiabatic logic, or 2LAL, could
dissipate as little as 1 eV of energy per transistor per cycle-
about 0.001 percent of the energy normally used by logic sig‑
nals in that generation of CMOS. Still, a practical reversible
computer has yet to be built using this or other approaches.
There's not much time left to develop reversible machines,
because progress in conventional semiconductor technol‑
ogy could grind to a halt soon. And if it does, the industry
could stagnate, making forward progress that much more
difficult. So the time is indeed ripe now to pursue this tech‑
nology, as it will probably take at least a decade for revers‑
ible computers to become practical.
The most crucial need is for new reversible device technolo‑
gies. Conventional CMOS transistors-especially the smallest,
state‑of‑the‑art ones-leak too much current to make very
efficient adiabatic circuits. Larger transistors based on older
manufacturing technology leak less, but they'd have to be
operated quite slowly, which means many devices would
need to be used to speed up computation through paral‑
lel operation. Stacking them in layers could yield compact
and energy‑efficient adiabatic circuits, but at the moment
such 3D fabrication is still quite costly. And CMOS may be
a dead end in any case.
Fortunately, there are some promising alternatives. One
is to use fast superconducting electronics to build reversi‑
ble circuits, which have already been shown to dissipate
less energy per device than the Landauer limit when oper‑
ated reversibly. Advances in this realm have been made by

researchers at Yokohama National University, Stony Brook
University, and Northrop Grumman. Meanwhile, a team led
by Ralph Merkle at the Institute for Molecular Manufactur‑
ing in Palo Alto, Calif., has designed reversible nanometer‑
scale molecular machines, which in theory could consume
one‑hundred‑billionth the energy of today's computing tech‑
nology while still switching on nanosecond timescales. The
rub is that the technology to manufacture such atomically
precise devices still needs to be invented.
Whether or not these particular approaches pan out, physi‑
cists who are working on developing new device concepts
need to keep the goal of reversible operation in mind. After
all, that is the only way that any new computing substrate
can possibly surpass the practical capabilities of end‑of‑line
CMOS technology by many orders of magnitude, as opposed
to only a few at most.

TO BE CLEAR, reversible computing is by no means easy.
Indeed, the engineering hurdles are enormous. Achieving
efficient reversible computing with any kind of technology
will likely require a thorough overhaul of our entire chip‑
design infrastructure. We'll also have to retrain a large part
of the digital‑engineering workforce to use the new design
methodologies. I would guess that the total cost of all of
the new investments in education, research, and develop‑
ment that will be required in the coming decades will most
likely run well up into the billions of dollars. It's a future‑
computing moon shot.
But in my opinion, the difficulty of these challenges would
be a very poor excuse for not facing up to them. At this
moment, we've arrived at a historic juncture in the evolution
of computing technology, and we must choose a path soon.
If we continue on our present course, this would amount
to giving up on the future of computing and accepting that
the energy efficiency of our hardware will soon plateau.
Even such unconventional concepts as analog or spike‑based
neural computing will eventually reach a limit if they are
not designed to also be reversible. And even a quantum‑
computing breakthrough would only help to significantly
speed up a few highly specialized classes of computations,
not computing in general.
But if we decide to blaze this new trail of reversible com‑
puting, we may continue to find ways to keep improving
computation far into the future. Physics knows no upper
limit on the amount of reversible computation that can be
performed using a fixed amount of energy. So as far as we
know, an unbounded future for computing awaits us, if we
are bold enough to seize it. n
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http://www.yoshilab.dnj.ynu.ac.jp/yoshilab_hp/index_english.html http://www.physics.sunysb.edu/Physics/RSFQ/index.html http://www.physics.sunysb.edu/Physics/RSFQ/index.html https://www.technologyreview.com/s/423405/superconducting-niobium-chip-smashes-silicon-power-consumption-standards/ http://www.imm.org/ https://en.wikipedia.org/wiki/K._Eric_Drexler http://www.imm.org/ http://www.halfbakedmaker.org/blog/58 http://www.halfbakedmaker.org/blog/58 http://ieeexplore.ieee.org/document/1059351/ https://en.wikipedia.org/wiki/Quantum_flux_parametron https://www.nd.edu/ https://www.nd.edu/ https://en.wikipedia.org/wiki/Quantum_dot_cellular_automaton https://en.wikipedia.org/wiki/Quantum_dot_cellular_automaton http://spectrum.ieee.org/semiconductors/design/neuromorphic-chips-are-destined-for-deep-learningor-obscurity http://spectrum.ieee.org/semiconductors/design/neuromorphic-chips-are-destined-for-deep-learningor-obscurity http://ieeexplore.ieee.org/document/5712162/?arnumber=5712162&tag=1 http://spectrum.ieee.org/reversiblecomputing0917 http://SPECTRUM.IEEE.ORG

Table of Contents for the Digital Edition of IEEE Spectrum September, 2017