Signal Delay in General RC Networks

Based upon the delay of Elmore, a single value of delay is derived for any node in a general RC network. The effects of parallel connections and stored charge are properly taken into consideration. A technique called tree decomposition and load redistribution is introduced that is capable of dealing with general RC networks without sacrificing a number of desirable properties of tree networks. An experimental simulator called SDS (Signal Delay Simulator) has been developed. For all the examples tested so far, this simulator runs two to three orders of magnitude faster than SPICE, and detects all transitions and glitches at approximately the correct time

Signal Delay in General RC Networks with Application to Timing Simulation of Digital Integrated Circuits

Modeling digital MOS circuits by RC networks has become a well accepted practice for estimating delays. In 1981, Penfield and Rubinstein proposed a method to bound the waveforms of nodes in an RC tree network. In this paper, a single value of delay is derived for any node in a general RC network. The effects of parallel connections and stored charges are properly taken into consideration. The algorithms can be used either as a stand-alone simulator, or as a front end for producing initial waveforms for waveform-relaxation based circuit simulators. An experimental simulator called SDS (Signal Delay Simulator) has been developed. For all the examples tested so far, this simulator runs two to three orders of magnitude faster than SPICE, and detects all transitions and glitches at approximately the correct time

Memristor models for machine learning

In the quest for alternatives to traditional CMOS, it is being suggested that digital computing efficiency and power can be improved by matching the precision to the application. Many applications do not need the high precision that is being used today. In particular, large gains in area- and power efficiency could be achieved by dedicated analog realizations of approximate computing engines. In this work, we explore the use of memristor networks for analog approximate computation, based on a machine learning framework called reservoir computing. Most experimental investigations on the dynamics of memristors focus on their nonvolatile behavior. Hence, the volatility that is present in the developed technologies is usually unwanted and it is not included in simulation models. In contrast, in reservoir computing, volatility is not only desirable but necessary. Therefore, in this work, we propose two different ways to incorporate it into memristor simulation models. The first is an extension of Strukov's model and the second is an equivalent Wiener model approximation. We analyze and compare the dynamical properties of these models and discuss their implications for the memory and the nonlinear processing capacity of memristor networks. Our results indicate that device variability, increasingly causing problems in traditional computer design, is an asset in the context of reservoir computing. We conclude that, although both models could lead to useful memristor based reservoir computing systems, their computational performance will differ. Therefore, experimental modeling research is required for the development of accurate volatile memristor models.Comment: 4 figures, no tables. Submitted to neural computatio

Self-organization of signal transduction

We propose a model of parameter learning for signal transduction, where the objective function is defined by signal transmission efficiency. We apply this to learn kinetic rates as a form of evolutionary learning, and look for parameters which satisfy the objective. This is a novel approach compared to the usual technique of adjusting parameters only on the basis of experimental data. The resulting model is self-organizing, i.e. perturbations in protein concentrations or changes in extracellular signaling will automatically lead to adaptation. We systematically perturb protein concentrations and observe the response of the system. We find compensatory or co-regulation of protein expression levels. In a novel experiment, we alter the distribution of extracellular signaling, and observe adaptation based on optimizing signal transmission. We also discuss the relationship between signaling with and without transients. Signaling by transients may involve maximization of signal transmission efficiency for the peak response, but a minimization in steady-state responses. With an appropriate objective function, this can also be achieved by concentration adjustment. Self-organizing systems may be predictive of unwanted drug interference effects, since they aim to mimic complex cellular adaptation in a unified way.Comment: updated version, 13 pages, 4 figures, 3 Tables, supplemental tabl

Traffic Control and Route Choice; Capacity Maximization and Stability

Peer reviewedPublisher PD

Delayed Dynamical Systems: Networks, Chimeras and Reservoir Computing

We present a systematic approach to reveal the correspondence between time delay dynamics and networks of coupled oscillators. After early demonstrations of the usefulness of spatio-temporal representations of time-delay system dynamics, extensive research on optoelectronic feedback loops has revealed their immense potential for realizing complex system dynamics such as chimeras in rings of coupled oscillators and applications to reservoir computing. Delayed dynamical systems have been enriched in recent years through the application of digital signal processing techniques. Very recently, we have showed that one can significantly extend the capabilities and implement networks with arbitrary topologies through the use of field programmable gate arrays (FPGAs). This architecture allows the design of appropriate filters and multiple time delays which greatly extend the possibilities for exploring synchronization patterns in arbitrary topological networks. This has enabled us to explore complex dynamics on networks with nodes that can be perfectly identical, introduce parameter heterogeneities and multiple time delays, as well as change network topologies to control the formation and evolution of patterns of synchrony