Assessing Random Dynamical Network Architectures for Nanoelectronics

Assessing Random Dynamical Network Architectures for Nanoelectronics

Abstract

Independent of the technology, it is generally expected that future nanoscale devices will be built from vast numbers of densely arranged devices that exhibit high failure rates. Other than that, there is little consensus on what type of technology and computing architecture holds most promises to go far beyond today’s top-down engineered silicon devices. Cellular automata (CA) have been proposed in the past as a possible class of architectures to the von Neumann computing architecture, which is not generally well suited for future massively parallel and fine-grained nanoscale electronics. While the top-down engineered semi-conducting technology favors regular and locally interconnected structures, future bottom-up self-assembled devices tend to have irregular structures because of the current lack of precise control over these processes. In this paper, we will assess random dynamical networks, namely Random Boolean Networks (RBNs) and Random Threshold Networks (RTNs), as alternative computing architectures and models for future information processing devices. We will illustrate that—from a theoretical perspective—they offer superior properties over classical CA-based architectures, such as inherent robustness as the system scales up, more efficient information processing capabilities, and manufacturing benefits for bottom-up designed devices, which motivates this investigation. We will present recent results on the dynamic behavior and robustness of such random dynamical networks while also including manufacturing issues in the assessment.

1Introduction and Motivation

The advent of multicore architectures and the slowdown of the processor’s operating frequency increase are signs that CMOS miniaturization is increasingly hitting fundamental physical limits. A key question is how computing architectures will evolve as we reach these fundamental limits. A likely possibility within the realm of CMOS technology is that the integration density will cease to increase at some point, instead only the number of components, i.e, the transistors, will further increase, which will necessarily lead to chips with a higher area. This trend can already be observed with multi-core architectures. That in itself has implications on the interconnect architecture, the power consumption and dissipation, and the reliability. Another possibility is to go beyond silicon-based technology and to change the computing and manufacturing paradigms, by using for example bottom-up self-assembled devices. Self-assembling nanowires [12] or carbon nanotube electronics [2] are promising candidates, although none of them has resulted in electronics that is able to compete with traditional CMOS so far. What seems clear is that the current way with build computers and the way we algorithmically solve problems with them may need to be fundamentally revisited, which this paper is all about.

While the top-down engineered CMOS technology favors regular and locally interconnected structures, future bottom-up self-assembled devices tend to have irregular structures because of the current lack of precise control over these processes. We therefore hypothesize that future and emerging computing architectures will be much more driven by manufacturing constraints and particularities than for CMOS, which allowed engineers to implement a logic-based computing architecture with extreme precision and reliability, at least in the past. Independent of the forthcoming device and fabrication technologies, it is generally expected that future nanoscale devices will be built from (1) vast numbers of densely arranged devices that (2) exhibit high failure rates. We take this working hypothesis for granted in this paper and address it from a perspective that focuses on the interconnect topology. This is justified by the fact that the importance of interconnects on electronic chips has outrun the importance of transistors as a dominant factor of performance [25]. The reasons are twofold: (1) the transistor switching speed for traditional silicon is much faster than the average wire delays and (2) the required chip area for interconnects has dramatically increased.

In [45], Zhirnov et al. explored integrated digital Cellular Automata (CA) architectures—which are highly regular structures with local interconnects (see Section 3)—as an alternative paradigms to the von Neumann computer architecture for future and emerging information processing devices. Here, we are interested to explore and assess a more general class of discrete dynamical systems, namely Random Boolean Networks (RBNs) and Random Threshold Networks (RTNs). We will mainly focus on RBNs, but RTNs are included in this paper because they offer an alternative paradigm to Boolean logic, which can be efficiently implemented as well (see Section 7).

Motivated by future and emerging nanoscale devices, we are interested to provide answers to the following questions:

  • Do RBNs and RTNs offer benefits over CA-architectures? If yes, what are they?

  • How does the interconnect complexity compare between RBNs/RTNs and CAs?

  • Does any of these architectures allow to solve problems more efficiently?

  • Is any of these architectures inherently more robust to simple errors?

  • Can CMOS and beyond-CMOS devices provide a benefit for the fabrication of any of these architectures?

We will argue and illustrate that—at least from a theoretical perspective—random dynamical networks offer superior properties over classical regular CA-based architectures, such as inherent robustness as the system scales up, more efficient information processing capabilities, and manufacturing benefits for bottom-up fabricated devices, which motivates this investigation. We will present recent results on the dynamic behavior and robustness of such random dynamical networks while also including manufacturing issues in the assessment.

To answer the above questions, we will extend recent results on the complex dynamical behavior of discrete random dynamical networks [34], their ability to solve problems [26], and novel interconnect paradigms [37].

The remainder of this paper is as following: Section 2 introduces random dynamical networks, namely random Boolean and random threshold networks. Section 3 briefly presents cellular automata architectures. Damage spreading and criticality of cellular automata and random dynamical networks is analyzed in Section Section 4. Section 5 analyzes the network topologies from a graph-theoretical and wiring-cost perspective. The task solving capabilities of RBNs and CAs are briefly assessed in Section 6, while Section 7 looks into manufacturing issues. Section 8 concludes the paper.

2Random Dynamical Networks

2.1Random Boolean Networks

A Random Boolean Network (RBN) [18] is a discrete dynamical system composed of nodes, also called automata, elements or cells. Each automaton is a Boolean variable with two possible states: , and the dynamics is such that

where , and each is represented by a look-up table of inputs randomly chosen from the set of nodes. Initially, neighbors and a look-table are assigned to each node at random. Note that (i.e., the fan-in) can refer to the exact or to the average number of incoming connections per node.

A node state is updated using its corresponding Boolean function:

These Boolean functions are commonly represented by lookup-tables (LUTs), which associate a -bit output (the node’s future state) to each possible -bit input configuration. The table’s out-column is called the rule of the node. Note that even though the LUTs of a RBN map well on an FPGA or other memory-based architectures, the random interconnect in general does not.

We randomly initialize the states of the nodes (initial condition of the RBN). The nodes are updated synchronously using their corresponding Boolean functions. Other updating schemes exist, see for example [13] for an overview. Synchronous random Boolean networks as introduced by Kauffman are commonly called NK networks or models. Figure 1 shows a possible NK random Boolean network representation ().

Figure 1: Illustration of a random Boolean network with N=8 nodes and K=3 inputs per node (self-connections are allowed). The node rules are commonly represented by lookup-tables (LUTs), which associate a 1-bit output (the node’s future state) to each possible K-bit input configuration. The table’s out-column is commonly called the rule of the node.
Figure 1: Illustration of a random Boolean network with nodes and inputs per node (self-connections are allowed). The node rules are commonly represented by lookup-tables (LUTs), which associate a -bit output (the node’s future state) to each possible -bit input configuration. The table’s out-column is commonly called the rule of the node.

2.2Random Threshold Networks

Random Threshold Networks (RTNs) are another type of discrete dynamical systems. An RTN consists of randomly interconnected binary sites (spins) with states . For each site , its state at time is a function of the inputs it receives from other spins at time :

with

The network sites are updated synchronously. In the following, the threshold parameter is set to zero. The interaction weights take discrete values or with equal probability. If does not receive signals from , one has .

3Cellular Automata Architectures

Cellular automata (CA) [44] were originally conceived by Ulam and von Neumann [41] in the 1940s to provide a formal framework for investigating the behavior of complex, extended systems. CAs are a special case of the more general class of random dynamical networks, in which space and time are discrete. A CA usually consists of a -dimensional regular lattice of lattice sites, commonly called nodes, cells, elements, or automata. Each cell can be in one of a finite number of possible states and further consists of a transition function (also called rule), which maps the neighboring states to the set of cell states. CAs are called uniform if all cells contain the same rule, otherwise they are non-uniform. Each cell takes as input the states of the cells within some finite local neighborhood. Here, we only consider non-uniform, two-dimensional (), folded, and binary CAs () with a radius- von Neumann neighborhood, where each cell is connected to each of its four immediate neighbors only. Figure Figure 2 illustrates such an CA. The Boolean functions in each node must therefore define possible input combinations. To be able to compare CAs with RBNs, we do not consider self-connections.

Figure 2: Illustration of a binary, 2D, folded cellular automaton with N=16 cells. Each node is connected to its four immediate neighbors (von Neumann neighborhood).
Figure 2: Illustration of a binary, 2D, folded cellular automaton with cells. Each node is connected to its four immediate neighbors (von Neumann neighborhood).

4Damage Spreading and Criticality

4.1Random Boolean and Threshold Networks

As we have seen in Section 2.2, RBNs and their complex dynamic behavior are essentially characterized by the average number of incoming links (fan-in) per node (e.g., Figure 1 shows a network with 3 incoming links per node). It turns out that in the thermodynamic limit, i.e., , RBNs exhibit a dynamical order-disorder transition at a sparse critical connectivity [10] (i.e., where each node receives on average two incoming connections from two randomly chosen other nodes), which partitions their operating space into 3 different regimes: (1), sub-critical, where , (2) complex, where , and (3) supercritical, where . In the sub-critical regime, the network dynamics are too “rigid” and the information processing capabilities are thus hindered, whereas in the supercritical regime, their behavior becomes chaotic. The complex regime is also commonly called the “edge of chaos,” because it represents the network connectivity where information processing is “optimal” and where a small number of stable attractors exist.

Similar observations were made for sparsely connected random threshold (neural) networks (RTN) [33] for . For a finite system size , the dynamics of both systems converge to periodic attractors after a finite number of updates. At , the phase space structure in terms of attractor periods [1], the number of different attractors [35] and the distribution of basins of attraction [3] is complex, showing many properties reminiscent of biological networks [20].

Results In [34] we have systematically studied and compared damage spreading (i.e., how a perturbed node-state influences the rest of the network nodes over time) at the sparse percolation (SP) limit for random Boolean and threshold networks with perturbations. In the SP limit, the damage induced in a network (i.e., by changing the state of a node) does not scale with system size. Obviously, this limit is relevant to information and damage propagation in many technological and natural networks, such as the Internet, disease spreading in populations, failure propagation in power grids, and networks-on-chips. We measure the damage spreading by the following methodology: the state of one randomly chosen node is changed. The damage is measured as the Hamming distance between a damaged and undamaged network instance after a large number of system updates.

We have shown that there is a characteristic average connectivity for RBNs and for RTNs, where the damage spreading of a single one-bit perturbation of a network node remains constant as the system size scales up. Figure Figure 3 illustrates this newly discovered point for RBNs and RTNs. For more details, see [34].

Figure 3: Average Hamming distance (damage) \langle d \rangle after 200 system updates, averaged over 10000 randomly generated networks for each value of \langle K \rangle, with 100 different random initial conditions and one-bit perturbed neighbor configurations for each network. For both RBN and RTN, all curves for different N approximately intersect in a characteristic point K_s.
Figure 3: Average Hamming distance (damage) after 200 system updates, averaged over 10000 randomly generated networks for each value of , with 100 different random initial conditions and one-bit perturbed neighbor configurations for each network. For both RBN and RTN, all curves for different approximately intersect in a characteristic point .

Discussion Both and are highly relevant for nano-scale electronics for the following reason: assuming we can build massive numbers of simple logic gates that implement a random Boolean function, the above findings tell us that on average, every gate should be connected somewhere close to both and in order to (1) guarantee optimal robustness against failures for any system size and (2) optimal information processing at the “edge of chaos.” We are also hypothesizing that natural systems, such as the brain or genetic regulatory networks, may have evolved towards these characteristic connectivities. This remains, however, to be proved and is part of ongoing research.

4.2Cellular Automata Damage Spreading

We have used the same approach as described above to measure the damage spreading in cellular automata. In order to vary the average number of incoming links per cell in a cellular automata (e.g., as pictured in Figure 2), we have adopted the following methodology: (1) for a desired average number of links per cell for a given CA size of cells, the total number of links in the automaton is given by ; (2) we then randomly choose possible connections on the regular CA-grid with uniform probability and establish the links. Damage is induced in the same way as for RBNs and RTNs: the state of one (or several) randomly chosen node(s) is changed. The damage is measured as the Hamming distance between a damaged and undamaged CA instance after a large number of system updates, in our case .

Results Figures Figure 4, Figure 5, and Figure 6 show the average damage of both RBNs and CAs for different system sizes and for a damage size of 1 and 10 respectively. We have left out RTNs for this analysis. As one can see, both the RBN and the CA average damage for different approximately intersect in the characteristic point . This point is less pronounced for the larger damage sizes (Figures Figure 5 and Figure 6). The RBN curves confirm what was already shown above in Figure 3, and are merely plotted here for comparison with the CA architectures and their system sizes imposed by square lattices.

Interestingly, the CAs show different damage propagation behavior for different system sizes and connectivities. First, we observe that the average damage for one-bit damage events (Figure Figure 4) is independent of the system size for up to approximatively average incoming connections per cell. This behavior disappears completely for large damage sizes (Figure 6). Second, Figure Figure 4 shows that all curves intersect at . Third, Figure Figure 6 suggest that for larger damage sizes, disappears for CAs. Fourth, the average damage for larger damage events, i.e., 10 and 20 in our examples, converges to the same final values for both RBNs and CAs as approaches 4.

Discussion We hypothesize that the particular behavior can be explained by the percolation limit of the cellular automata. Da Silva et al. [8] found that the link probability at the percolation limit is approximatively , which means that the average connectivity at the percolation limit in our CA topology with a maximum of 4 neighbors is given by . This value corresponds to the experimentally observed value where the damage spreading suddenly becomes dependent of the system size. Because of the local CA connectivity, there are lots of disconnected components below the percolation limit. Below this limit, the damage spreading is thus very slow and limited by the disconnected components, reason why it is essentially independent of system size. Above the percolation limit, the CA suddenly becomes connected and damage spreading becomes therefore dependent on the system size. For larger damage events, such as 10 or 20, damage becomes more dependent on system size even below the percolation limit because there is a higher probability that damage is induced in several disconnected components at the same time.

In summary: for single-node damage events, CAs offer system-size independent damage spreading for up to about (which corresponds to the percolation limit), however, this particular behavior disappears for larger damage events. We conclude that in the general case, CAs do not possess a characteristic connectivity , where damage spreading is independent of the system size . Such a connectivity, however, exists for both RBNs and RTNs, which makes them particularly suitable as a computing model in an environment with high error probabilities or systems with low system component reliabilities. An example are logical gates based on bio-molecular components [4], where high failure rates can be expected.

Figure 4: Average Hamming distance (damage) \langle d \rangle after 200 system updates, averaged over 100 randomly generated networks for each value of \langle K \rangle, with 100 different random initial conditions and a damage size of 1 node for each network. See text for discussion.
Figure 4: Average Hamming distance (damage) after 200 system updates, averaged over 100 randomly generated networks for each value of , with 100 different random initial conditions and a damage size of 1 node for each network. See text for discussion.
Figure 5: Average Hamming distance (damage) \langle d \rangle after 200 system updates, averaged over 100 randomly generated networks for each value of \langle K \rangle, with 100 different random initial conditions and a damage size of 10 nodes for each network. See text for discussion.
Figure 5: Average Hamming distance (damage) after 200 system updates, averaged over 100 randomly generated networks for each value of , with 100 different random initial conditions and a damage size of 10 nodes for each network. See text for discussion.
Figure 6: Average Hamming distance (damage) \langle d \rangle after 200 system updates, averaged over 100 randomly generated networks for each value of \langle K \rangle, with 100 different random initial conditions and a damage size of 20 nodes for each network. See text for discussion.
Figure 6: Average Hamming distance (damage) after 200 system updates, averaged over 100 randomly generated networks for each value of , with 100 different random initial conditions and a damage size of 20 nodes for each network. See text for discussion.

5Complex Networks and Wiring Costs

Most real networks, such as brain networks [36], electronic circuits [17], the Internet, and social networks share the so-called small-world (SW) property [43]. Compared to purely locally and regularly interconnected networks (such as for example the CA interconnect of Figure 2), small-world networks have a very short average distance (measured as the number of edges to traverse) between any pair of nodes, which makes them particularly interesting for efficient communication.

The classical Watts-Strogatz small-world network [43] is built from a regular lattice with only nearest neighbor connections. Every link is then rewired with a rewiring probability to a randomly chosen node. Thus, by varying , one can obtain a fully regular () and a fully random () network topology. The rewiring procedure establishes “shortcuts” in the network, which significantly lower the average distance (i.e., the number of edges to traverse) between any pair of nodes. In the original model, the length distribution of the shortcuts is uniform since a node is chosen randomly. If the rewiring of the connections is done proportional to a power law, , where is the wire length, then we obtain a small-world power-law network. The exponent affects the network’s communication characteristics [23] and navigability [21], which is better than in the uniformly generated small-world network. One can think of other distance-proportional distributions for the rewiring, such as for example a Gaussian distribution, which has been found between certain layers of the rat’s neocortical pyramidal neurons [14].

In a real network, it is fair to assume that local connections have a lower cost (in terms of the associated wire-delay and the area required) than long-distance connections. Physically realizing small-world networks with uniformly distributed long-distance connections is thus not realistic and distance, i.e., the wiring cost, needs to be taken into account, a perspective that recently gained increasing attention [30]. On the other hand, a network’s topology also directly affects how efficient problems can be solved.

Teuscher [37] has pragmatically and experimentally investigated important design trade-offs and properties of an irregular, abstract, yet physically plausible 3D small-world interconnect fabric that is inspired by modern network-on-chip paradigms. The results confirm that (1) computation in irregular assemblies is a promising and disruptive computing paradigm for self-assembled nano-scale electronics and (2) that 3D small-world interconnect fabrics with a power-law decaying distribution of shortcut lengths are physically plausible and have major advantages over local 2D and 3D regular topologies, such as CA interconnects.

Discussion There is a trade-off between (1) the physical realizability and (2) the communication characteristics for a network topology. A locally and regularly interconnected topology, such as that of a CA, is in general easy to build (especially for to-down engineered CMOS technology) and only involves minimal wire and area cost (as for example shown by Zhirnov et al. [45]), but it offers poor global communication characteristics and scales-up poorly with system size. On the other hand, a random topology, such as that of RBNs or RTNs, scales-up well and has a very short-average path length, but it is not physically plausible because it involves costly long-distance connections established independently of the Euclidean distance between the nodes. The RBN and RTN topologies we consider here as thus extremes, such as CA topologies, the ideal lies in between: small-world topologies with a distance-dependent distribution of the connectivity. Such topologies are located in a unique spot in the design space and also offer two other highly relevant properties [22]: (1) efficient navigability and thus potentially efficient routing, and (2) robustness against random link removals. For these reasons, we can conclude that small-world graphs are the most promising interconnects for future massive scale devices.

6A Glance on Task Solving

In [26], Mesot and Teuscher have presented a novel analytical approach to find the local rules of random Boolean networks to solve the global density classification and the synchronization task—which are well known benchmark tasks in the CA community—from any initial configuration. They have also quantitatively and qualitatively compared the results with previously published work on cellular automata and have shown that randomly interconnected automata are computationally more efficient in solving these two global tasks.

In addition, preliminary results by the authors [39] also suggest that RBN generalize better on simple learning tasks than sub-critical or supercritical networks, but more research will be necessary.

Discussion To efficiently solve algorithmic problems with distributed computing architectures, efficient communication is key. This is particularly true for tasks such as the density or the synchronization task, which are trivial to solve if one has a global view on the entire system state, but non-trivial to solve if each cell only sees a limited number of neighboring cells. It is thus not surprising that cells interconnected by a network with the small-world property perform much better on such tasks because the information propagation is significantly better. This is a too often neglected fact for CAs, in particular if one wants to use them as a viable mainstream and general purpose computing architecture. It is well-know that even simple CAs are computationally universal (and so are RBNs), i.e., they can solve any algorithmic problem, but due to their local non-small-world interconnect topology, that will only be possible in a highly inefficient way in the general case, i.e., for a large set of different applications. This is well illustrated with the (highly inefficient) implementation of a universal Turing machine on top of the Game of Life [32]. Naturally, there are exceptions to the general case, and it has been shown that CAs can be extremely efficient for certain niche applications, such as for examle image processing.

7Manufacturing Issues

As Chen et al. [6] state, “[i]n order to realize functional nano-electronic circuits, researchers need to solve three problems: invent a nanoscale device that switches an electric current on and off; build a nanoscale circuit that controllably links very large numbers of these devices with each other and with external systems in order to perform memory and/or logic functions; and design an architecture that allows the circuits to communicate with other systems and operate independently on their lower-level details.”

While we can currently build switching devices in various technologies besides CMOS (see [47] for an overview), one of the remaining challenges is to assemble and interconnect these switching devices (or logic functions) to larger systems, and ultimately to design a computing architecture that allows to perform reliable computations. As mentioned before, there is little consensus in the research community on what type of technology and computing architecture holds most promises for the future.

The motivation for investigating randomly assembled interconnects and computing architectures can be summarized by the following observations:

  • long-range and global connections are costly (in terms of wire delay and of the chip area used) and limit system performance [15];

  • it is unclear whether a precisely regular and homogeneous arrangement of components is needed and possible on a multi-billion-component or even Avogadro-scale assembly of nano-scale components [40]

  • “[s]elf-assembly makes it relatively easy to form a random array of wires with randomly attached switches” [46]; and

  • building a perfect system is very hard and expensive

We have hypothesized in [38] and [37] that bottom-up self-assembled electronics based on conductive nanowires or nanotubes can lead to the random interconnect topologies we are interested in, however, several questions remain open and are part of a 3-year interdisciplinary research project at LANL. Our approach consists in using a hybrid assembly (as others explore as well, e.g., [12]), where the functional building blocks will still be traditional silicon in a first step, while the interconnect is made up from self-assembled nanowires. Nanowires can be grown in various ways using diverse materials, such as metals and semiconductors. We have chosen a novel way to grow conductive nanowires, which Wang et al. [42] at LANL have pioneered and demonstrated: Ag nanowires can be fabricated on top of conducting polyaniline polymer membranes via a spontaneous electrodeless deposition (self-assembly) method. We hypothesize that this will allow to densely interconnect silicon components in a simple and cheap way with specific distance-dependent wire-length distributions. We believe that this approach will ultimately allow us to easily and cheaply fabricate RBN-like computing architectures.

Random threshold networks, on the other hand, could be rather straightforwardly and efficiently implemented with resonant tunneling diode (RTD) logic circuits (see e.g., [31]), and represent a very interesting alternative to conventional Boolean logic gates. The reported results in this paper on random threshold networks can thus directly be applied to the implementation of such devices. There has been a significant body of research in the area of threshold logic in the past (see e.g., [27]), but to the best of our knowledge, random threshold networks have not been considered as computing models for future and emerging computing machines.

8Conclusion

The central claim of this paper is that locally interconnected computing architectures, such as cellular automata (CA), are in general not appropriate models for large-scale and general-purpose computations. We have supported this claim with recent theoretical results on the complex dynamical behavior of discrete random dynamical networks, their robustness to damage events as the system scales up, their ability to efficiently solve tasks, and their improved transport characteristics due to the short average path length. The arguments, in a nutshell, why we believe that CAs are not promising architectures for future information-processing devices, are as following:

  • their local interconnect topology is not small-world and has thus worse global transport characteristics (than small-world or random graphs), which directly affects the effectiveness of how general-purpose algorithmic tasks can be solved;

  • in terms of a complex dynamical system, they operate in the supercritical regime () with the widely used von Neumann neighborhood, which makes them sensitive to initial conditions;

  • they do not generally have a characteristic connectivity , where damage spreading is independent of system size, which makes a system inherently robust; and

  • it is unclear whether a precisely regular and homogeneous arrangement of components is possible at the scale of future information processing devices.

We have assessed RBNs and RTNs as alternative models, however, as we have seen in Section 5 they come at a serious cost: the uniform probability to establish connections with any node in the system independent of the Euclidean distance between them is not physically plausible and too expensive in terms of wiring cost. The ultimate interconnect topology is small-world and has a distance-dependent distribution of the wires [37]. We have preliminary evidence that, if we were to connect RBNs and RTNs by such a network topology, both and would still exist. Research to clarify this question is under progress,

Open Questions and Unaddressed Issues Naturally, there are a number of open questions and issues that we have not addressed because they are beyond the scope of this paper. In particular, an irregular topology with random logical functions makes the mapping of a given digital circuit much harder, if not impossible in certain cases. On the other hand, a regular interconnect topology clearly makes the mapping task easier. We believe, however, that this challenge can be addressed by automated design tools. After all, computation in random assemblies is not completely new and has been more or less successfully tried by others, e.g., [28], however in different contexts and with a different perspective in mind than we have presented here.

We have deliberately not focused on any particular application in this paper because our results are independent of the application. However, it is noteworthy that locally interconnected CAs have been proven to outperform other general purpose architecture on very specific applications. A good example are cellular neural networks (CNNs) [7], which, e.g., allow to perform certain imagine processing tasks orders of magnitude faster than any other machine.

Further, it is unknown at this point how exactly our findings fit into the interconnect predictions made by Rent’s rule, however, the rule may not be applicable to our non-traditional circuits since it is based on empirical results. Further research on this is planned.

Last but not least, we would like to mention that, although we have only considered 2D arrangements and interconnects here for simplicity, the future is clearly 3D (e.g., see [29]). The main reason is that the average wire length in 3D is shorter than in 2D interconnects.

Outlook We believe that computation in random self-assemblies of simple components and interconnections is a highly appealing paradigm, both from the perspective of fabrication as well as performance and robustness. Future work will focus on (1) the manufacturing issues, (2) appropriate design methodologies, (3) addressing the mapping issues, and (4) more realistic models, which will allow to better assess the performance and cost, and (5) specific applications.

Acknowledgments

We gratefully acknowledge the support of the U.S. Department of Energy through the LANL/LDRD Program for this work. The authors would like to thank Elshan A. Akhadov and Hsing-Lin Wang.

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