Thesis Defense Announcement
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		   Reversibility for Efficient Computing
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I review the ultimate limits that fundamental physics places on information
processing, with particular attention to the implications of
thermodynamics.  I advocate the study of models of computation that give
the best asymptotic scaling behavior that is physically possible.  I argue
that one such model will be 3-D meshes of some class of {\emph{reversible}}
computing elements.  I show that such meshes scale better than {\emph{any}}
physically possible machine based on traditional irreversible computing
techniques, by factors that are polynomial in the machine size.

Next, I review reversible computing techniques based on semiconductor
technology, and present a proof-of-concept mesh processor based on these
techniques.  With present-day technology, I project that reversible
techniques do not improve supercomputing performance/cost below a
multi-billion-dollar cost level.  But reversible techniques can still be
useful in the near-term, in low-energy applications.

In the longer term, device technology improvements (such as
lower-resistance switches) will only improve the "reversible advantage" in
supercomputing.  I review a number of proposals that have been made for
nano-scale logic devices.  Under some of these proposals, we can show that
any computer larger than a few microns to millimeters across will be faster
if it is mostly reversible.

Finally, I discuss issues in the design of processor instruction sets,
programming languages, and algorithms for reversible machines.  In general,
to attain optimal asymptotic efficiency, it seems that the underlying
reversibility of physics must be exposed at every level, all the way up to
the algorithm design.  I show how the primitives at each level should be
chosen so as not to reduce the asymptotic efficiency that can be achieved.
I illustrate straightforward reversible algorithms for a variety of
problems, including a reversible simulation of quantum mechanics.