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Reversible computing is a model of computing where the computational process to some extent is reversible, i.e., time-invertible. In a computational model that uses transitions from one state of the abstract machine to another, a necessary condition for reversibility is that the relation of the mapping from states to their successors must be one-to-one. Reversible computing is generally considered an unconventional form of computing.
There are two major, closely related, types of reversibility that are of particular interest for this purpose: physical reversibility and logical reversibility.
A process is said to be physically reversible if it results in no increase in physical entropy; it is isentropic. These circuits are also referred to as charge recovery logic, adiabatic circuits, or adiabatic computing. Although in practice no nonstationary physical process can be exactly physically reversible or isentropic, there is no known limit to the closeness with which we can approach perfect reversibility, in systems that are sufficiently well-isolated from interactions with unknown external environments, when the laws of physics describing the system's evolution are precisely known.
Probably the largest motivation for the study of technologies aimed at actually implementing reversible computing is that they offer what is predicted to be the only potential way to improve the energy efficiency of computers beyond the fundamental von Neumann-Landauer limit of kT ln(2) energy dissipated per irreversible bit operation. (Though even the Landauer limit is millions of times below the energy consumption of modern computers.)
As was first argued by Rolf Landauer of IBM, in order for a computational process to be physically reversible, it must also be logically reversible. Landauer's principle is the loosely formulated notion that the erasure of n bits of information must always incur a cost of nk ln(2) in thermodynamic entropy. A discrete, deterministic computational process is said to be logically reversible if the transition function that maps old computational states to new ones is a one-to-one function; i.e. the output logical states uniquely defines the input logical states of the computational operation.
For computational processes that are nondeterministic (in the sense of being probabilistic or random), the relation between old and new states is not a single-valued function, and the requirement needed to obtain physical reversibility becomes a slightly weaker condition, namely that the size of a given ensemble of possible initial computational states does not decrease, on average, as the computation proceeds forwards.
The reversibility of physics and reversible computing Landauer's principle (and indeed, the second law of thermodynamics itself) can also be understood to be a direct logical consequence of the underlying reversibility of physics, as is reflected in the general Hamiltonian formulation of mechanics, and in the unitary time-evolution operator of quantum mechanics more specifically.
In the context of reversible physics, the phenomenon of entropy increase (and the observed arrow of time) can be understood to be consequences of the fact that our evolved predictive capabilities are rather limited, and cannot keep perfect track of the exact reversible evolution of complex physical systems, especially since these systems are never perfectly isolated from an unknown external environment, and even the laws of physics themselves are still not known with complete precision. Thus, we (and physical observers generally) always accumulate some uncertainty about the state of physical systems, even if the system's true underlying dynamics is a perfectly reversible one that is subject to no entropy increase if viewed from a hypothetical omniscient perspective in which the dynamical laws are precisely known.
The implementation of reversible computing thus amounts to learning how to characterize and control the physical dynamics of mechanisms to carry out desired computational operations so precisely that we can accumulate a negligible total amount of uncertainty regarding the complete physical state of the mechanism, per each logic operation that is performed. In other words, we would need to precisely track the state of the active energy that is involved in carrying out computational operations within the machine, and design the machine in such a way that the majority of this energy is recovered in an organized form that can be reused for subsequent operations, rather than being permitted to dissipate into the form of heat.
Although achieving this goal presents a significant challenge for the design, manufacturing, and characterization of ultra-precise new physical mechanisms for computing, there is at present no fundamental reason to think that this goal cannot eventually be accomplished, allowing us to someday build computers that generate much less than 1 bit's worth of physical entropy (and dissipate much less than kT ln 2 energy to heat) for each useful logical operation that they carry out internally.
The motivation behind much of the research that has been done in reversible computing was the first seminal paper on the topic, which was published by Charles H. Bennett of IBM research in 1973. Today, the field has a substantial body of academic literature behind it. A wide variety of reversible device concepts, logic gates, electronic circuits, processor architectures, programming languages, and application algorithms have been designed and analyzed by physicists, electrical engineers, and computer scientists.
This field of research awaits the detailed development of a high-quality, cost-effective, nearly reversible logic device technology, one that includes highly energy-efficient clocking and synchronization mechanisms. This sort of solid engineering progress will be needed before the large body of theoretical research on reversible computing can find practical application in enabling real computer technology to circumvent the various near-term barriers to its energy efficiency, including the von Neumann-Landauer bound. This may only be circumvented by the use of logically reversible computing, due to the Second Law of Thermodynamics.
To implement reversible computation, estimate its cost, and to judge its limits, it is formalized it in terms of gate-level circuits. For example, the inverter (logic gate) (NOT) gate is reversible because it can be undone. The exclusive or (XOR) gate is irreversible because its inputs cannot be unambiguously reconstructed from an output value. However, a reversible version of the XOR gate—the controlled NOT gate (CNOT)—can be defined by preserving one of the inputs. The three-input variant of the CNOT gate is called the Toffoli gate. It preserves two of its inputs a,b and replaces the third c by . With , this gives the AND function, and with this gives the NOT function. Thus, the Toffoli gate is universal and can implement any reversible Boolean function (given enough zero-initialized ancillary bits). More generally, reversible gates have the same number of inputs and outputs. A reversible circuit connects reversible gates without fanouts and loops. Therefore, such circuits contain equal numbers of input and output wires, each going through an entire circuit.
Reversible logic circuits have been first motivated in the 1960s by theoretical considerations of zero-energy computation as well as practical improvement of bit-manipulation transforms in cryptography and computer graphics. Since the 1980s, reversible circuits have attracted interest as components of quantum algorithms, and more recently in photonic and nano-computing technologies where some switching devices offer no signal gain.
- Reverse computation
- Reversible dynamics
- Maximum entropy thermodynamics, on the uncertainty interpretation of the second law of thermodynamics
- Reversible process
- Toffoli gate
- Fredkin gate
- Quantum computing
- Billiard-ball computer
- Three-input universal logic gate
- Reversible cellular automaton
- J. von Neumann, Theory of Self-Reproducing Automata, Univ. of Illinois Press, 1966.
- R. Landauer, "Irreversibility and heat generation in the computing process," IBM Journal of Research and Development, vol. 5, pp. 183-191, 1961.
- C. H. Bennett, "Logical reversibility of computation," IBM Journal of Research and Development, vol. 17, no. 6, pp. 525-532, 1973.
- C. H. Bennett, "The Thermodynamics of Computation -- A Review," International Journal of Theoretical Physics, vol. 21, no. 12, pp. 905-940, 1982.
- Rolf Drechsler, Robert Wille. From Truth Tables to Programming Languages: Progress in the Design of Reversible Circuits. International Symposium on Multiple-Valued Logic, 2011. http://www.informatik.uni-bremen.de/agra/doc/konf/11_ismvl_reversible_circuit_design_tutorial.pdf
- Mehdi Saeedi, Igor L. Markov, Synthesis and Optimization of Reversible Circuits - A Survey, ACM Computing Surveys, 2012. http://arxiv.org/abs/1110.2574
- Rolf Drechsler and Robert Wille. Reversible Circuits: Recent Accomplishments and Future Challenges for an Emerging Technology. International Symposium on VLSI Design and Test, 2012. http://www.informatik.uni-bremen.de/agra/doc/konf/2012_vdat_reversible_circuits_accompl_chall.pdf
Review of later theoretical work: P.M.B. Vitanyi, Time, space, and energy in reversible computing, Proceedings of the 2nd ACM conference on Computing frontiers, 2005, 435–444. Introduction to Reversible Computing by Kalyan S. Perumalla
- Introductory article on reversible computing
- First International Workshop on reversible computing
- Recent publications of Michael P. Frank
- Internet Archive backup of the "Reversible computing community Wiki" that was administered by Dr. Frank
- Recent Workshops on Reversible Computation
- Open-source toolkit for reversible circuit design