26,578 research outputs found
The Synthesis and Analysis of Stochastic Switching Circuits
Stochastic switching circuits are relay circuits that consist of stochastic
switches called pswitches. The study of stochastic switching circuits has
widespread applications in many fields of computer science, neuroscience, and
biochemistry. In this paper, we discuss several properties of stochastic
switching circuits, including robustness, expressibility, and probability
approximation.
First, we study the robustness, namely, the effect caused by introducing an
error of size \epsilon to each pswitch in a stochastic circuit. We analyze two
constructions and prove that simple series-parallel circuits are robust to
small error perturbations, while general series-parallel circuits are not.
Specifically, the total error introduced by perturbations of size less than
\epsilon is bounded by a constant multiple of \epsilon in a simple
series-parallel circuit, independent of the size of the circuit.
Next, we study the expressibility of stochastic switching circuits: Given an
integer q and a pswitch set S=\{\frac{1}{q},\frac{2}{q},...,\frac{q-1}{q}\},
can we synthesize any rational probability with denominator q^n (for arbitrary
n) with a simple series-parallel stochastic switching circuit? We generalize
previous results and prove that when q is a multiple of 2 or 3, the answer is
yes. We also show that when q is a prime number larger than 3, the answer is
no.
Probability approximation is studied for a general case of an arbitrary
pswitch set S=\{s_1,s_2,...,s_{|S|}\}. In this case, we propose an algorithm
based on local optimization to approximate any desired probability. The
analysis reveals that the approximation error of a switching circuit decreases
exponentially with an increasing circuit size.Comment: 2 columns, 15 page
Comprehensive and modular stochastic modeling framework for the variability-aware assessment of Signal Integrity in high-speed links
This paper presents a comprehensive and modular modeling framework for stochastic signal integrity analysis of complex high-speed links. Such systems are typically composed of passive linear networks and nonlinear, usually active, devices. The key idea of the proposed contribution is to express the signals at the ports of each of such system elements or subnetworks as a polynomial chaos expansion. This allows one to compute, for each block, equivalent deterministic models describing the stochastic variations of the network voltages and currents. Such models are synthesized into SPICE-compatible circuit equivalents, which are readily connected together and simulated in standard circuit simulators. Only a single circuit simulation of such an equivalent network is required to compute the pertinent statistical information of the entire system, without the need of running a large number of time-consuming electromagnetic circuit co-simulations. The accuracy and efficiency of the proposed approach, which is applicable to a large class of complex circuits, are verified by performing signal integrity investigations of two interconnect examples
Genetic noise control via protein oligomerization
Gene expression in a cell entails random reaction events occurring over
disparate time scales. Thus, molecular noise that often results in phenotypic
and population-dynamic consequences sets a fundamental limit to biochemical
signaling. While there have been numerous studies correlating the architecture
of cellular reaction networks with noise tolerance, only a limited effort has
been made to understand the dynamic role of protein-protein interactions. Here
we have developed a fully stochastic model for the positive feedback control of
a single gene, as well as a pair of genes (toggle switch), integrating
quantitative results from previous in vivo and in vitro studies. We find that
the overall noise-level is reduced and the frequency content of the noise is
dramatically shifted to the physiologically irrelevant high-frequency regime in
the presence of protein dimerization. This is independent of the choice of
monomer or dimer as transcription factor and persists throughout the multiple
model topologies considered. For the toggle switch, we additionally find that
the presence of a protein dimer, either homodimer or heterodimer, may
significantly reduce its random switching rate. Hence, the dimer promotes the
robust function of bistable switches by preventing the uninduced (induced)
state from randomly being induced (uninduced). The specific binding between
regulatory proteins provides a buffer that may prevent the propagation of
fluctuations in genetic activity. The capacity of the buffer is a non-monotonic
function of association-dissociation rates. Since the protein oligomerization
per se does not require extra protein components to be expressed, it provides a
basis for the rapid control of intrinsic or extrinsic noise
Stochastic switching circuit synthesis
Shannon in his 1938 Masterpsilas Thesis demonstrated that any Boolean function can be realized by a switching relay circuit, leading to the development of deterministic digital logic. Here, we replace each classical switch with a probabilistic switch (pswitch). We present algorithms for synthesizing circuits closed with a desired probability, including an algorithm that generates optimal size circuits for any binary fraction. We also introduce a new duality property for series-parallel stochastic switching circuits. Finally, we construct a universal probability generator which maps deterministic inputs to arbitrary probabilistic outputs. Potential applications exist in the analysis and design of stochastic networks in biology and engineering
Tensor Computation: A New Framework for High-Dimensional Problems in EDA
Many critical EDA problems suffer from the curse of dimensionality, i.e. the
very fast-scaling computational burden produced by large number of parameters
and/or unknown variables. This phenomenon may be caused by multiple spatial or
temporal factors (e.g. 3-D field solvers discretizations and multi-rate circuit
simulation), nonlinearity of devices and circuits, large number of design or
optimization parameters (e.g. full-chip routing/placement and circuit sizing),
or extensive process variations (e.g. variability/reliability analysis and
design for manufacturability). The computational challenges generated by such
high dimensional problems are generally hard to handle efficiently with
traditional EDA core algorithms that are based on matrix and vector
computation. This paper presents "tensor computation" as an alternative general
framework for the development of efficient EDA algorithms and tools. A tensor
is a high-dimensional generalization of a matrix and a vector, and is a natural
choice for both storing and solving efficiently high-dimensional EDA problems.
This paper gives a basic tutorial on tensors, demonstrates some recent examples
of EDA applications (e.g., nonlinear circuit modeling and high-dimensional
uncertainty quantification), and suggests further open EDA problems where the
use of tensor computation could be of advantage.Comment: 14 figures. Accepted by IEEE Trans. CAD of Integrated Circuits and
System
Network Synthesis of Linear Dynamical Quantum Stochastic Systems
The purpose of this paper is to develop a synthesis theory for linear
dynamical quantum stochastic systems that are encountered in linear quantum
optics and in phenomenological models of linear quantum circuits. In
particular, such a theory will enable the systematic realization of
coherent/fully quantum linear stochastic controllers for quantum control,
amongst other potential applications. We show how general linear dynamical
quantum stochastic systems can be constructed by assembling an appropriate
interconnection of one degree of freedom open quantum harmonic oscillators and,
in the quantum optics setting, discuss how such a network of oscillators can be
approximately synthesized or implemented in a systematic way from some linear
and non-linear quantum optical elements. An example is also provided to
illustrate the theory.Comment: Revised and corrected version, published in SIAM Journal on Control
and Optimization, 200
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