Analog Computation
A special issue of Computers (ISSN 2073-431X).
Deadline for manuscript submissions: closed (31 October 2009)
Special Issue Editor
Associate Professor, School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA
Special Issue Information
Dear Colleagues,
It is curious that an issue of Algorithms should be devoted to analog computing, whose essence, a difficult-to-quantify analogy, is the opposite of discrete and precisely stated algorithms. Even the study of analog computers had almost vanished by 1973, when Theodore Nelson, the inventor of hypertext, wrote in Computer Lib/Dream
“ANALOG COMPUTERS DISPOSED OF“There are two kinds of computers: analog and digital. (Also hybrid, meaning a combination.) Analog computers are so unimportant compared to digital computers that we will polish them off in a couple of paragraphs.
And so he did. For many years afterward, until 1986, the fossil remnants of analog computers—operational amplifiers—were found in special purpose processors used in military and aerospace applications and in top-of-the-line electronic synthesizers, such as those designed and built by Robert Moog. However, in 1986 Carver Mead revived interest in special-purpose analog computers to model neural systems. His work showed that analog designs could model the retina, the cochlea and other biological systems. The circuits designed by Mead and his associates easily solved tasks using continuous data that were difficult and time-consuming for digital computers that manipulated discrete data algorithmically.
In 1993 Lee A. Rubel described a new analog computing paradigm, the extended analog compute (EAC)r. Rubel added mathematical components to Claude Shannon’s general purpose analog computer (GPAC), the machines “disposed of” by Nelson. The ideal EAC could solve problems involving partial differential equations that Shannon’s GPAC could not solve directly. Rubel believed that his machine could not be built, and in its ideal form it cannot. But research since 1993 at Indiana University has produced real EACs that have been applied successfully to a wide variety of problems ranging from the biological systems modeled by Mead to, most recently, solutions to the quantum applications of Grover and Deutsch-Josza. Today an EAC supercomputer is being simulated (as a hybrid machine) that models the natural processes of protein folding.
This work is not occurring in a vacuum. Computer scientists worldwide are exploring analog computing under such names as amorphous computing, unconventional computing, computing with bulk matter, non-silicon computing and other designations. Biologists and computer scientists team up to build “computers” out of neural tissue or slime mold. Physicists design new materials, such as graphemes, whose molecular properties are analogous to the atomic-level quantum behavior. Theoretical computer scientists investigate the complexity of analog computing, and speculate on new complexity classes. All of this emerging work has resulted from the limits that physical law places of digital computers. Moore’s Law—the inability to double the number of transistors on an integrated circuit every 18 months—has lead researchers to look for alternatives the digital computer. The analog computer is the most easily accessible of the possibilities, since some kinds can by constructed out of Jell-O® brand gelatin—and have been!
For this special issue we invite researchers to submit papers on the following topics, and related topics not specifically mentioned:
In 1993 Lee A. Rubel described a new analog computing paradigm, the extended analog compute (EAC)r. Rubel added mathematical components to Claude Shannon’s general purpose analog computer (GPAC), the machines “disposed of” by Nelson. The ideal EAC could solve problems involving partial differential equations that Shannon’s GPAC could not solve directly. Rubel believed that his machine could not be built, and in its ideal form it cannot. But research since 1993 at Indiana University has produced real EACs that have been applied successfully to a wide variety of problems ranging from the biological systems modeled by Mead to, most recently, solutions to the quantum applications of Grover and Deutsch-Josza. Today an EAC supercomputer is being simulated (as a hybrid machine) that models the natural processes of protein folding.
This work is not occurring in a vacuum. Computer scientists worldwide are exploring analog computing under such names as amorphous computing, unconventional computing, computing with bulk matter, non-silicon computing and other designations. Biologists and computer scientists team up to build “computers” out of neural tissue or slime mold. Physicists design new materials, such as graphemes, whose molecular properties are analogous to the atomic-level quantum behavior. Theoretical computer scientists investigate the complexity of analog computing, and speculate on new complexity classes. All of this emerging work has resulted from the limits that physical law places of digital computers. Moore’s Law—the inability to double the number of transistors on an integrated circuit every 18 months—has lead researchers to look for alternatives the digital computer. The analog computer is the most easily accessible of the possibilities, since some kinds can by constructed out of Jell-O® brand gelatin—and have been!
For this special issue we invite researchers to submit papers on the following topics, and related topics not specifically mentioned:
- analog, hybrid analog–digital, and other continuous computers
- amorphous, unconventional and non-silicon continuous systems
- quantum, biological and chemical analog and hybrid systems
- theory of analog and hybrid analog-digital computation
- physics of analog and hybrid analog-digital computation
- analysis of the concept of analogy, including the complexity of analogy
- complexity of analog computation, including Kolmogorov complexity
- application and configuration of analog and hybrid computers
- efficient digital algorithms to simulate analog and hybrid systems
- experimental results from real analog and hybrid computers
As analog computers experience a modern renaissance, the concept of analogy becomes ever more important. Even though it is as central to the analog computer as the algorithm is to the digital computer, analogy has only barely begun to be studied and quantified. This special issue of Algorithms is important because it may bring researchers in both the digital and analog computing paradigms together to study an emerging part of the discipline of computation.
Jonathan W. Mills, Ph. D.
Guest Editor
Keywords
- analogy
- amorphous computer
- analog computer
- biological computer
- extended analog computer
- general-purpose analog computer
- hybrid digital-analog computer
- neural computer
- non-silicon computer
- unconventional computer
- quantum analog computer
- bulk matter
- graphenes
- analog complexity
- continuous systems complexity
- digital simulation
- algorithmic complexity
- Kolmogorov complexity
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