Access the full text.
Sign up today, get an introductory month for just $19.
Y. Benenson, Binyamin Gil, Uri Ben-Dor, R. Adar, E. Shapiro (2004)
An autonomous molecular computer for logical control of gene expressionNature, 429
Charles Bennett (1973)
Logical reversibility of computationIbm Journal of Research and Development, 17
D. Goldberg (1988)
Genetic Algorithms in Search Optimization and Machine Learning
D. Woods, T. Naughton (2005)
An optical model of computationTheor. Comput. Sci., 334
G. Nagy (1963)
A Survey of Analog Memory DevicesIEEE Trans. Electron. Comput., 12
D. Aharonov (1998)
Quantum Computation
Dominik Schultes (2006)
Rainbow Sort: Sorting at the Speed of LightNatural Computing, 5
Joshua Arulanandham, Cristian Calude, M. Dinneen (2002)
Bead-Sort: A Natural Sorting AlgorithmBull. EATCS, 76
Anastasios Vergis, K. Steiglitz, B. Dickinson (1986)
The complexity of analog computationMathematics and Computers in Simulation, 28
J. Hartmanis (1995)
On the Weight of ComputationsBull. EATCS, 55
J. Ngo, Joe Marks (1992)
Computational complexity of a problem in molecular structure prediction.Protein engineering, 5 4
John Reif, Akhilesh Tyagi (1997)
Efficient parallel algorithms for optical computing with the discrete Fourier transform (DFT) primitive.Applied optics, 36 29
P. Crescenzi, Deborah Goldman, C. Papadimitriou, A. Piccolboni, M. Yannakakis (1998)
On the Complexity of Protein FoldingJournal of computational biology : a journal of computational molecular cell biology, 5 3
A. Andrew (2009)
Quantum computing, WikisKybernetes, 38
P. Ball (2011)
Physics of life: The dawn of quantum biologyNature, 474
A. Fraenkel (1997)
Protein folding, spin glass and computational complexity
J. Goodman (1982)
Architectural development of optical data processing systems
P. Shor (1994)
Algorithms for quantum computation: discrete logarithms and factoringProceedings 35th Annual Symposium on Foundations of Computer Science
N. Shaked, Stéphane Messika, S. Dolev, Joseph Rosen (2007)
Optical solution for bounded NP-complete problems.Applied optics, 46 5
T. Haist, W. Osten (2007)
An optical solution for the traveling salesman problem.Optics express, 15 16
L. Adleman (1994)
Molecular computation of solutions to combinatorial problems.Science, 266 5187
B. Eggers (2016)
Computers And Intractability A Guide To The Theory Of Np Completeness
H. Iwamura, M. Akazawa, Y. Amemiya (1997)
Single-Electron Majority Logic CircuitsIEICE technical report. Electron devices, 97
S. Cook (1971)
The complexity of theorem-proving proceduresProceedings of the third annual ACM symposium on Theory of computing
Mihai Oltean (2007)
Solving the Hamiltonian path problem with a light-based computerNatural Computing, 7
H. Caulfield (2008)
Optics Goes Where No Electronics Can Go: Zero-Energy-Dissipation Logic
Mihai Oltean (2006)
Unconventional Computation, 5th International Conference, UC 2006, York, UK, September 4-8, 2006, Proceedings, 4135
cerf's up DOI:10.1145/2666093 Vinton G. Cerf Unconventional Computing The August 2014 issue of IEEE Spectrum had two articles of interest related to computing: "Silicon's Second Act" and "Spin Memory Shows Its Might." On top of that, in the last couple of years, IBM has demonstrated two remarkable achievements: The Watson Artificial Intelligence system and the August 8, 2014 cover story of Science entitled "Brain Inspired Chip." The TrueNorth chipset and the programming language it uses have demonstrated remarkable power efficiency compared to more conventional processing elements. What all of these topics have in common for me is the prospect of increasingly unconventional computing methods that may naturally force us to rethink how we analyze problems for purposes of getting computers to solve them for us. I consider this to be a refreshing development, challenging the academic, research, and practitioner communities to abandon or adapt past practices and to consider new ones that can take advantage of new technologies and techniques. It has always been my experience that half the battle in problem solving is to express the problem in such a way the solution may suggest itself. In mathematics, it is often the case that a change of variables can dramatically restructure the way in which the problem or formula is presented; leading one to find related problems whose solutions may be more readily applied. Changing from Cartesian to Polar coordinates often dramatically simplifies its expression. For example, a Cartesian equation for a circle centered at (0,0) is X2 + Y2 = Z2 but the polar version is simply r()= a for some value of a. It may prove to be the case that the computational methods for solving problems with quantum computers, neural chips, and Watson-like systems will admit very different strategies and tactics than those applied in more conventional architectures. The use of graphics processing units (GPUs) to solve problems, rather than generating textured triangles at high speed, has already forced programmers to think differently about the way in which they express and compute their results. The parallelism of the GPUs and their ability to process many small "programs" at once has made them attractive for evolutionary or genetic programming, for example. One question is: Where will these new technologies take us? We have had experiences in the past with unusual designs. The Connection Machine designed by Danny Hillis was one of the first really large-scale computing machines (65K one-bit processors) hyperconnected together. LISP was one of the programming languages used for the Connection Machines along with URDU, among others. This brings to mind the earlier LISP machines made by Symbolics and LISP Machines, Inc., among others. The rapid advance in speed of more conventional processors largely overtook the advantage of special purpose, potentially language-oriented computers. This was particularly evident with the rise of the so-called RISC (Reduced Instruction Set Computing) machines developed by John Hennessy (the MIPS system) and David Patterson (Berkeley RISC and Sun Microsystems SPARC), among many others. David E. Shaw, at Columbia University, pioneered one of the explorations into a series of designs of a single instruction stream, multiple data stream (SIMD) supercomputer he called Non-Von (for "non-Von-Neumann"). Using single-bit arithmetic logic units, this design has some relative similarity to the Connection Machine although their interconnection designs were quite different. It has not escaped my attention that David Shaw is now the chief scientist of D.E. Shaw Research and is focused on computational biochemistry and bioinformatics. This topic also occupies his time at Columbia University, where he holds a senior research fellowship and adjunct professorship. Returning to new computing and memory technologies, one has the impression the limitations of conventional use of silicon technology may be overcome with new materials and with new architectural designs as is beginning to be apparent with the new IBM Neural chip. I have only taken time to offer an very incomplete and sketchy set of observations about unconventional computing in this column, but I think it is arguable that in this second decade of the 21st century, we are starting to see serious opportunities for rethinking how we may compute. Vinton G. Cerf is vice president and Chief Internet Evangelist at Google. He served as ACM president from 20122014. Copyright held by author. O C TO B E R 2 0 1 4 | VO L. 57 | N O. 1 0 | C OM M U N IC AT ION S OF THE ACM
Communications of the ACM – Association for Computing Machinery
Published: Sep 23, 2014
You can share this free article with as many people as you like with the url below! We hope you enjoy this feature!
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get an introductory month for just $19.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.