Massively parallel computing on an organic molecular layer

Technology Review – Japanese scientists have built a cellular automaton from individual molecules that carries out huge numbers of calculations in parallel They’ve laid down 300 DDQ molecules on a gold substrate, setting them up as a cellular automaton. More impressive still, they’ve then initialised the system so that it “calculates” the way heat diffuses in a conducting medium and the way cancer spreads through tissue. And since the entire layer is involved in the calculation, this a massively parallel computation using a single layer of organic molecules.

Bandyopadhyay and co say the key feature of this type of calculation is the fact that one DDQ molecule can link to many others, rather like neurons in the brain. “Generalization of this principle would…open up a new vista of emergent computing using an assembly of molecules,” they say.

The concept of a wireless molecular circuit: a. The DDQ molecule. b. The DDQ bilayer’s atomic structure; side view (above); top view (below, Movie 1). T denotes a molecule on the top layer

A massively parallel computing on an organic molecular layer, A. Bandyopadhyay, R. Pati, S. Sahu, F. Peper, D. Fujita, Nature Physics 6, 369 (2010). (25 pages)

Current computers operate at enormous speeds of ~10^13 bits per second, but their principle of sequential logic operation has remained unchanged since the 1950s. Though our brain is much slower on a per-neuron base (~10^3 firings per second), it is capable of remarkable decision-making based on the collective operations of millions of neurons at a time in ever-evolving neural circuitry. Here we use molecular switches to build an assembly where each molecule communicates–like neurons–with many neighbors simultaneously. The assembly’s ability to reconfigure itself spontaneously for a new problem allows us to realize conventional computing constructs like logic gates and Voronoi decompositions, as well as to reproduce two natural phenomena: heat diffusion and the mutation of normal cells to cancer cells. This is a shift from the current static computing paradigm of serial bit-processing to a regime in which a large number of bits are processed in parallel in dynamically changing hardware

In order to realize a CA (cellular automata) that can carry out a wide variety of computational tasks, it is necessary to obtain a sufficient level of control on the transition state dynamics. The DDQ (2,3-dichloro-5,6-dicyano-p-benzoquinone molecule) CA in this article provides an intriguing avenue to achieve such control while still relying on a relatively small and simple molecule. It is not only the interactions of the molecules in the DDQ CA but also the subtle formation of circuits in the molecular top-layer that count in facilitating transitions between states. The evolution of circuits is dependent on an easy-to-control parameter: the charge density in an area. These circuits dramatically influence the dominance of transition rules, as we have observed, and offer an efficient way to influence the computational behavior of the CA. The robust functioning of local circuits originates from the CA cell’s one-to-many communication and interaction at a time. Generalization of this principle would change the existing concept of static circuit-based electronics and open up a new vista of emergent computing using an assembly of molecules

Discrete logic-state transport rules:
Rule 1: Convergent Universe: A charge moves a distance d towards PPC (left). To calculate PPC, we neglect those areas that have charge(s) in between (right). Rule 2: Creation of spatial Δx and temporal limit Δt: The <Δx> (experiment) and <Δt> (simulation) are plotted for four surfaces initially covered with state 0s, 1s, 2s, or 3s. Rule 3: Divergent Universe: Examples of collisions; arrows denote the direction of motion of logic-states. Rule 4: Life of logic states: Trail of state 2 for motion of state 1 and 3 (top); the death of a cluster of state 2 (bottom). Rule 5: Collapse of space: Four examples of group formation of state 1s, 3s. Rule 6: Transformation: Fusion of two state 1s to create a state 3 (left); breaking of state 3 to create two state 1s (right). Rule 7: Priority of Rules: Correlating input patterns, initial circuits and the dominant Rules (see Movie 4 for details and SI text online for the algorithm to program these rules).

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