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Register a new userAssessing Random Dynamical Network Architectures for Nanoelectronics
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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 todays 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 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 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.
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Cancer invasion and metastasis depend on angiogenesis. The cellular processes (growth, migration, and apoptosis) that occur during angiogenesis are tightly regulated by signaling molecules. Thus, understanding how cells synthesize multiple biochemical signals initiated by key external stimuli can lead to the development of novel therapeutic strategies to combat cancer. In the face of large amounts of disjoint experimental data generated from multitudes of laboratories using various assays, theoretical signal transduction models provide a framework to distill this vast amount of data. Such models offer an opportunity to formulate and test new hypotheses, and can be used to make experimentally verifiable predictions. This study is the first to propose a network model that highlights the cross-talk between the key receptors involved in angiogenesis, namely growth factor, integrin, and cadherin receptors. From available experimental data, we construct a stochastic Boolean network model of receptor cross-talk, and systematically analyze the dynamical stability of the network under continuous-time Boolean dynamics with a noisy production function. We find that the signal transduction network exhibits a robust and fast response to external signals, independent of the internal cell state. We derive an input-output table that maps external stimuli to cell phenotypes, which is extraordinarily stable against molecular noise with one important exception: an oscillatory feedback loop between the key signaling molecules RhoA and Rac1 is unstable under arbitrarily low noise, leading to erratic, dysfunctional cell motion. Finally, we show that the network exhibits an apoptotic response rate that increases with noise, suggesting that the probability of programmed cell death depends on cell health.
With the scaling of technology and higher requirements on performance and functionality, power dissipation is becoming one of the major design considerations in the development of network processors. In this paper, we use an assertion-based methodology for system-level power/performance analysis to study two dynamic voltage scaling (DVS) techniques, traffic-based DVS and execution-based DVS, in a network processor model. Using the automatically generated distribution analyzers, we analyze the power and performance distributions and study their trade-offs for the two DVS policies with different parameter settings such as threshold values and window sizes. We discuss the optimal configurations of the two DVS policies under different design requirements. By a set of experiments, we show that the assertion-based trace analysis methodology is an efficient tool that can help a designer easily compare and study optimal architectural configurations in a large design space.
We systematically study and compare damage spreading at the sparse percolation (SP) limit for random boolean and threshold networks with perturbations that are independent of the network size $N$. This limit is relevant to information and damage propagation in many technological and natural networks. Using finite size scaling, we identify a new characteristic connectivity $K_s$, at which the average number of damaged nodes $bar d$, after a large number of dynamical updates, is independent of $N$. Based on marginal damage spreading, we determine the critical connectivity $K_c^{sparse}(N)$ for finite $N$ at the SP limit and show that it systematically deviates from $K_c$, established by the annealed approximation, even for large system sizes. Our findings can potentially explain the results recently obtained for gene regulatory networks and have important implications for the evolution of dynamical networks that solve specific computational or functional tasks.
This paper describes the architecture, the development and the implementation of Janus II, a new generation application-driven number cruncher optimized for Monte Carlo simulations of spin systems (mainly spin glasses). This domain of computational physics is a recognized grand challenge of high-performance computing: the resources necessary to study in detail theoretical models that can make contact with experimental data are by far beyond those available using commodity computer systems. On the other hand, several specific features of the associated algorithms suggest that unconventional computer architectures, which can be implemented with available electronics technologies, may lead to order of magnitude increases in performance, reducing to acceptable values on human scales the time needed to carry out simulation campaigns that would take centuries on commercially available machines. Janus II is one such machine, recently developed and commissioned, that builds upon and improves on the successful JANUS machine, which has been used for physics since 2008 and is still in operation today. This paper describes in detail the motivations behind the project, the computational requirements, the architecture and the implementation of this new machine and compares its expected performances with those of currently available commercial systems.
Earthquake network is known to be complex in the sense that it is scale-free, small-world, hierarchically organized and assortatively mixed. Here, the time evolution of earthquake network is analyzed around main shocks in the context of the community structure. It is found that the maximum of the modularity measure quantifying existence of communities exhibits a peculiar behavior: its maximum value stays at a large value before a main shock, suddenly drops to a small value at the main shock, and then increases to relax to a large value again relatively slowly. Thus, a main shock absorbs and merges communities to create a larger community, showing how a main shock can be characterized in the complex-network representation of seismicity.
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