High performance FPGA implementation of the mersenne twister

Efficient generation of random and pseudorandom sequences is of great importance to a number of applications [4]. In this paper, an efficient implementation of the Mersenne Twister is presented. The proposed architecture has the smallest footprint of all published architectures to date and occupies only 330 FPGA slices. Partial pipelining and sub-expression simplification has been used to improve throughput per clock cycle. The proposed architecture is implemented on an RC1000 FPGA Development platform equipped with a Xilinx XCV2000E FPGA, and can generate 20 million 32 bit random numbers per second at a clock rate of 24.234 MHz. A through performance analysis has been performed, and it is observed that the proposed architecture clearly outperforms other existing implementations in key comparable performance metrics

Novel sparse OBC based distributed arithmetic architecture for matrix transforms

Inner product (IP) forms the basis of a number of signal processing algorithms and applications such as transforms, filters, communication systems etc. Distributed arithmetic (DA) provides an effective methodology to implement IP of vectors and matrices using a simple combination of memory elements, adders and shifters instead of lumped multipliers. This bit level rearrangement results in much higher computational efficiencies and yields compact designs highly suited for high performance resource constrained applications. Offset binary coding (OBC) is an effective technique to further optimize the DA, and allows us to reduce the memory requirements by a factor of two, with minimum additional computational complexity. This makes OBC-DA attractive for applications that are both resource and memory constrained. In addition, sparse matrix factorization techniques can be exploited to further reduce the size of the DA-ROMs. In this paper, the design and implementation of a novel OBC based DA is demonstrated using a generic architecture for implementing discrete orthogonal transforms (DOTs). Implementation is performed on the Xilinx Virtex-II Pro field programmable gate array (FPGA), and a detailed comparison between conventional and OBC based DA is presented to highlight the trade offs in various design metrics including performance, area and power

Thoughts about God in Siddha Songs

Siddhars are unique in the field of Tamil literature and in the history of Tamil religious thoughts. Siddha literature is in Tamil language and is practised largely in Tamil speaking part of India. Innovative ideas about religion can be found in Siddha literature. It is a fact that the history of Tamil literature shows us that although Siddhars performed and prescribed medicine, they also performed ideologies. The Siddhars were clear minded on the principle of God. They are revolutionaries who cannot be contained within specific boundaries.  This article examines how the religious thoughts of the Siddhars are different in the multi-religious environment

A committee machine gas identification system based on dynamically reconfigurable FPGA

This paper proposes a gas identification system based on the committee machine (CM) classifier, which combines various gas identification algorithms, to obtain a unified decision with improved accuracy. The CM combines five different classifiers: K nearest neighbors (KNNs), multilayer perceptron (MLP), radial basis function (RBF), Gaussian mixture model (GMM), and probabilistic principal component analysis (PPCA). Experiments on real sensors' data proved the effectiveness of our system with an improved accuracy over individual classifiers. Due to the computationally intensive nature of CM, its implementation requires significant hardware resources. In order to overcome this problem, we propose a novel time multiplexing hardware implementation using a dynamically reconfigurable field programmable gate array (FPGA) platform. The processing is divided into three stages: sampling and preprocessing, pattern recognition, and decision stage. Dynamically reconfigurable FPGA technique is used to implement the system in a sequential manner, thus using limited hardware resources of the FPGA chip. The system is successfully tested for combustible gas identification application using our in-house tin-oxide gas sensors

Rank-Sparsity Incoherence for Matrix Decomposition

Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix into its sparse and low-rank components. Such a problem arises in a number of applications in model and system identification, and is NP-hard in general. In this paper we consider a convex optimization formulation to splitting the specified matrix into its components, by minimizing a linear combination of the $\ell_1$ norm and the nuclear norm of the components. We develop a notion of \emph{rank-sparsity incoherence}, expressed as an uncertainty principle between the sparsity pattern of a matrix and its row and column spaces, and use it to characterize both fundamental identifiability as well as (deterministic) sufficient conditions for exact recovery. Our analysis is geometric in nature, with the tangent spaces to the algebraic varieties of sparse and low-rank matrices playing a prominent role. When the sparse and low-rank matrices are drawn from certain natural random ensembles, we show that the sufficient conditions for exact recovery are satisfied with high probability. We conclude with simulation results on synthetic matrix decomposition problems

Diagonal and Low-Rank Matrix Decompositions, Correlation Matrices, and Ellipsoid Fitting

In this paper we establish links between, and new results for, three problems that are not usually considered together. The first is a matrix decomposition problem that arises in areas such as statistical modeling and signal processing: given a matrix $X$ formed as the sum of an unknown diagonal matrix and an unknown low rank positive semidefinite matrix, decompose $X$ into these constituents. The second problem we consider is to determine the facial structure of the set of correlation matrices, a convex set also known as the elliptope. This convex body, and particularly its facial structure, plays a role in applications from combinatorial optimization to mathematical finance. The third problem is a basic geometric question: given points $v_1,v_2,...,v_n\in \R^k$ (where $n > k$) determine whether there is a centered ellipsoid passing \emph{exactly} through all of the points. We show that in a precise sense these three problems are equivalent. Furthermore we establish a simple sufficient condition on a subspace $U$ that ensures any positive semidefinite matrix $L$ with column space $U$ can be recovered from $D+L$ for any diagonal matrix $D$ using a convex optimization-based heuristic known as minimum trace factor analysis. This result leads to a new understanding of the structure of rank-deficient correlation matrices and a simple condition on a set of points that ensures there is a centered ellipsoid passing through them.Comment: 20 page