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An Advanced Architecture of a Massive Parallel Processing Nano Brain Operating 100 Billion Molecular Neurons Simultaneously

An Advanced Architecture of a Massive Parallel Processing Nano Brain Operating 100 Billion Molecular Neurons Simultaneously

Anirban Bandyopadhyay, Subrata Ghosh, Daisuke Fujita, Ranjit Pati, Satyajit Sahu
Copyright: © 2014 |Pages: 33
ISBN13: 9781466651258|ISBN10: 1466651253|EISBN13: 9781466651265
DOI: 10.4018/978-1-4666-5125-8.ch076
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MLA

Bandyopadhyay, Anirban, et al. "An Advanced Architecture of a Massive Parallel Processing Nano Brain Operating 100 Billion Molecular Neurons Simultaneously." Nanotechnology: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2014, pp. 1588-1620. https://doi.org/10.4018/978-1-4666-5125-8.ch076

APA

Bandyopadhyay, A., Ghosh, S., Fujita, D., Pati, R., & Sahu, S. (2014). An Advanced Architecture of a Massive Parallel Processing Nano Brain Operating 100 Billion Molecular Neurons Simultaneously. In I. Management Association (Ed.), Nanotechnology: Concepts, Methodologies, Tools, and Applications (pp. 1588-1620). IGI Global. https://doi.org/10.4018/978-1-4666-5125-8.ch076

Chicago

Bandyopadhyay, Anirban, et al. "An Advanced Architecture of a Massive Parallel Processing Nano Brain Operating 100 Billion Molecular Neurons Simultaneously." In Nanotechnology: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 1588-1620. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-5125-8.ch076

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Abstract

Molecular machines (MM, Badjic, 2004; Collier, 2000; Jian & Tour, 2003; Koumura & Ferringa, 1999; Ding & Seeman, 2006) may resolve three distinct bottlenecks of scientific advancement (Bandyopadhyay, Fujita, Pati, 2008). Nanofactories (Phoenix, 2003) composed of MM may produce atomically perfect products spending negligible amount of energy (Hess, 2004) thus alleviating the energy crisis. Computers made by MM operating thousands of bits at a time may match biological processors mimicking creativity and intelligence (Hall, 2007), thus far considered as the prerogative of nature. State-of-the-art brain surgeries are not yet fatal-less, MMs guided by a nano-brain may execute perfect bloodless surgery (Freitas, 2005). Even though all three bottlenecks converge to a single necessity of nano-brain, futurists and molecular engineers have remained silent on this issue. Our recent invention of 16 bit parallel processor is a first step in this direction (Bandyopadhyay, 2008). However, the device operates inside ultra-high vacuum chamber. For practical application, one needs to design a 3 D standalone architecture. Here, we identify the minimum nano-brain functions for practical applications and try to increase the size from 2 nm to 20 µm. To realize this, three major changes are made. First, central control unit (CCU) and external execution units (EU) are modified so that they process information independently, second, CCU instructs EU the basic rules of information processing; third, once rules are set CCU does not hinder EU-computation. The basic design of the proposed nano-brain is a dendrimer (Hawker, 2005; Galliot, 1997; Devadoss, 2001; Quintana, 2002; Peer, 2007), with a control unit at its core and a molecular cellular neural network (m-CNN, Rosca, 1993; Chua, 2005) or Cellular Automata (CA, Wolfram, 1983) on its outer surface (EU). Each CNN/CA cell mimics the functionality of neurons by processing multiple bits reversibly (Rozenberg, 2004; Li, 2004; Bandyopadhyay, 2004). We have designed a megamer (Tomalia, 2005) consisting of dendrimer (~10 nm) as its unit CNN cell for building the giant 100 billion neuron based nano brain architecture. An important spontaneous control from 10 nm to 20 µm is achieved by an unique potential distribution following r=a sin k??, where r is the co-ordinate of doped neuron cluster, k is the branch number, ? is the angle of deviation and a is a constant typical of the megamer architecture.

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