1,087 research outputs found
Scalable Inference for Markov Processes with Intractable Likelihoods
Bayesian inference for Markov processes has become increasingly relevant in
recent years. Problems of this type often have intractable likelihoods and
prior knowledge about model rate parameters is often poor. Markov Chain Monte
Carlo (MCMC) techniques can lead to exact inference in such models but in
practice can suffer performance issues including long burn-in periods and poor
mixing. On the other hand approximate Bayesian computation techniques can allow
rapid exploration of a large parameter space but yield only approximate
posterior distributions. Here we consider the combined use of approximate
Bayesian computation (ABC) and MCMC techniques for improved computational
efficiency while retaining exact inference on parallel hardware
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
Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.Peer reviewe
Compensation of biased excitation effects for MLS-based nonlinear systems' identification
MLS-based identification of nonlinear systems is largely affected by deviations in the excitation signal amenable to the combined effect of DC-offset and an arbitrary gain. These induce orthogonality loss in the MLS filter bank output, thus invalidating the underlying identification construction. In this paper we present a correction algorithm to derive the corrected Volterra kernels from the biased estimations provided by the standard MLS-based procedure
Simulation based sequential Monte Carlo methods for discretely observed Markov processes
Parameter estimation for discretely observed Markov processes is a
challenging problem. However, simulation of Markov processes is straightforward
using the Gillespie algorithm. We exploit this ease of simulation to develop an
effective sequential Monte Carlo (SMC) algorithm for obtaining samples from the
posterior distribution of the parameters. In particular, we introduce two key
innovations, coupled simulations, which allow us to study multiple parameter
values on the basis of a single simulation, and a simple, yet effective,
importance sampling scheme for steering simulations towards the observed data.
These innovations substantially improve the efficiency of the SMC algorithm
with minimal effect on the speed of the simulation process. The SMC algorithm
is successfully applied to two examples, a Lotka-Volterra model and a
Repressilator model.Comment: 27 pages, 5 figure
Efficient FPGA implementations of volterra DFES for optical systems
In this work suitable architectures and high-throughput FPGA implementations of Volterra Decision Feedback Equalizers (VDFEs) for optical communication links are presented. Two VDFE configurations were selected based on the available resources of the employed FPGA devices, and two multiplexer-based architectures were developed for each of them in order to achieve the target throughput. The comparison of the experimental results with respect to different VDFE configurations, throughput, and FPGA devices points out the platform-specific design characteristics. The introduced architectures meet the desired 10Gb/s throughput, so it is demonstrated that the FPGA is a suitable platform for high-speed optical fiber communication systems
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