Cellular Wave Computing in Nanoscale via Million Processor Chips

A bifurcation is emerging in computer science and engineering due to the sudden emergence of many-core or even kilo-processor chips on the market. Due to the physical limitations, in CMOS technologies below 65 nm, a drastic power dissipation limit, a major signal propagation speed and distance limit, and a distributed character of the circuit elements are forcing new architectures. As a result, locality, the local connectedness becomes a prevailing property, the cellular, i.e., mainly locally connected processor arrays are becoming the norm, and the cellular wave dynamics can produce unique and practical effects.

[1]  Leon O. Chua,et al.  Cellular Neural Networks and Visual Computing , 2002 .

[2]  István Szatmári,et al.  Object comparison using PDE‐based wave metric on cellular neural networks , 2006, Int. J. Circuit Theory Appl..

[3]  Servando Espejo,et al.  Ultra-high frame rate focal plane image sensor and processor , 2002 .

[4]  William Aspray,et al.  Papers of John Von Neumann on computing and computer theory, Vol 12 , 1986 .

[5]  Wolfgang Porod,et al.  Bio‐Inspired Nano‐Sensor‐Enhanced CNN Visual Computer , 2004, Annals of the New York Academy of Sciences.

[6]  Ákos Zarándy,et al.  Cellular multiadaptive analogic architecture: a computational framework for UAV applications , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[7]  Tamás Roska,et al.  The CNN universal machine: an analogic array computer , 1993 .

[8]  Tamás Roska,et al.  Circuits, computers, and beyond Boolean logic , 2007, Int. J. Circuit Theory Appl..

[9]  Ronald Tetzlaff,et al.  Automated detection of a preseizure state: non‐linear EEG analysis in epilepsy by Cellular Nonlinear Networks and Volterra systems , 2006, Int. J. Circuit Theory Appl..

[10]  István Petrás,et al.  Implementing the Multilayer Retinal Model on the Complex-Cell CNN-um Chip Prototype , 2004, Int. J. Bifurc. Chaos.

[11]  Tamás Roska.  Computational And Computer Complexity Of Analogic Cellular Wave Computers , 2003, J. Circuits Syst. Comput..

[12]  Janne Roos.  Speed-up and performance evaluation of piecewise-linear DC analysis , 2007, Int. J. Circuit Theory Appl..

[13]  M. Ercsey-Ravasz,et al.  Stochastic simulations on the cellular wave computers , 2006 .

[14]  Ángel Rodríguez-Vázquez,et al.  ACE16k: the third generation of mixed-signal SIMD-CNN ACE chips toward VSoCs , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[15]  T. Roska,et al.  Cellular wave computers for brain-like spatial-temporal sensory computing , 2005, IEEE Circuits and Systems Magazine.

[16]  Tamás Roska,et al.  A CNN framework for modeling parallel processing in a mammalian retina , 2002, Int. J. Circuit Theory Appl..

[17]  Philipp Fischer,et al.  Automated detection of a preseizure state: non-linear EEG analysis in epilepsy by Cellular Nonlinear Networks and Volterra systems: Research Articles , 2006 .

[18]  Tamás Roska,et al.  Function‐in‐layout: a demonstration with bio‐inspired hyperacuity chip , 2007, Int. J. Circuit Theory Appl..

[19]  A. Turing.  The chemical basis of morphogenesis , 1952, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences.

[20]  A. Zarandy,et al.  Bi-i: a standalone ultra high speed cellular vision system , 2005, IEEE Circuits and Systems Magazine.

[21]  Zoltán Fodróczi,et al.  Computational auditory scene analysis in cellular wave computing framework , 2006, Int. J. Circuit Theory Appl..

[22]  Tamás Roska,et al.  Cellular wave computers for nanotera- scale technology---beyond boolean, spatial-temporal logic in million processor devices , 2007 .

[23]  Johan A. K. Suykens,et al.  Learning of spatiotemporal behaviour in cellular neural networks , 2006, Int. J. Circuit Theory Appl..

[24]  F. Werblin,et al.  Vertical interactions across ten parallel, stacked representations in the mammalian retina , 2001, Nature.