Unconventional computing is computing by any of a wide range of new or unusual methods. It is also known as alternative computing.
The term of "unconventional computation" was coined by Cristian S. Calude and John Casti and used at the "First International Conference on Unconventional Models of Computation", held in Auckland, New Zealand in 1998.
The general theory of computation allows for a variety of models. Historically, however, computing technology first developed using mechanical methods, and eventually evolved into using electronic techniques, which remain the state-of-the-art. Further development may require development of new technologies.[why?]
Mechanical computers retain some interest today both in research and as analogue computers. Some mechanical computers have a theoretical or didactic relevance, such as billiard-ball computers, while hydraulic ones like the MONIAC or the Water integrator were used effectively.
While some are actually simulated, others are not[clarification needed]. No attempt is made[dubious ] to build a functioning computer through the mechanical collisions of billiard balls. The domino computer is another theoretically interesting mechanical computing scheme.[why?]
Electronic digital computers
Most modern computers are electronic computers with the Von Neumann architecture based on digital electronics, with extensive integration made possible following the invention of the transistor and the scaling of Moore's law.
Unconventional computing is, according to a[which?] conference description, "an interdisciplinary research area with the main goal to enrich or go beyond the standard models, such as the Von Neumann computer architecture and the Turing machine, which have dominated computer science for more than half a century". These methods model their computational operations based on non-standard paradigms, and are currently mostly in the research and development stage.
These are unintuitive and pedagogical examples that a computer can be made out of almost anything.
Reservoir computing is a computational framework in the context of machine learning. The main advantage of this unconventional computing framework is that it facilitates a simple and fast learning algorithm in addition to a hardware implementation, known as a physical reservoir computer. After the input signal is fed into the reservoir, which is treated as a "black box," a simple readout mechanism is trained to read the state of the reservoir and map it to the desired output.
Optical computing uses light to compute.
Fluid Computing is a method of computing that uses a fluid, such as liquids or inert gases, in order to create logic elements. This can be applied in both analog and digital methods.
Quantum computing, perhaps the most well-known and developed unconventional computing method, is characterized by the use of quantum superposition in order to perform functions similar to that of a standard computer.
Some biological approaches are heavily inspired by the behavior of neurons. This method of calculation has achieved limited success, most notably in 1999, where a simple neurocomputer was created by William Ditto of the Georgia Institute of Technology.
Cellular automata and amorphous computing
Analog computing uses the continuous variation aspect of physical phenomena such as electrical, mechanical, or hydraulic quantities (analog signals) to model the problem being solved. A few examples are the slide rule which can perform multiplication, division, trigonometry, etc., the Antikythera mechanism which calculated the positions of planets and the Moon, the planimeter, a mechanical integrator for calculating the area of an arbitrary 2D shape.
- Network computing (disambiguation)
- WDR paper computer
- MONIAC hydraulic computer
- r/UnconventionalCompute subreddit
- "Unconventional Models of Computation 1998".
- C.S. Calude. "Unconventional Computing: A Brief Subjective History, CDMTCS Report 480, 2015".
- Penrose, Roger: The Emperor's New Mind. Oxford University Press, 1990. See also corresponding article on it.
- "Unconventional computation Conference 2007".
- Tanaka, Gouhei; Yamane, Toshiyuki; Héroux, Jean Benoit; Nakane, Ryosho; Kanazawa, Naoki; Takeda, Seiji; Numata, Hidetoshi; Nakano, Daiju; Hirose, Akira (2019-07-01). "Recent advances in physical reservoir computing: A review". Neural Networks. 115: 100–123. doi:10.1016/j.neunet.2019.03.005. ISSN 0893-6080.