Kenneth Steiglitz

Bio: Kenneth Steiglitz is an academic researcher from Princeton University. The author has contributed to research in topics: Signal processing & Very-large-scale integration. The author has an hindex of 46, co-authored 202 publications receiving 14495 citations. Previous affiliations of Kenneth Steiglitz include Telcordia Technologies & Northwestern University.

Reconfigurability and reliability of systolic/wavefront arrays

Abstract: Fault-tolerant redundant structures for maintaining reliable arrays are studied. It is assumed that the desired array (application graph) is embedded in a certain class of regular, bounded-degree graphs called dynamic graphs. The authors define the degree of reconfigurability (DR), and DR with distance DR/sup d/ of a redundant graph. When DR (respectively DR/sup d/) is independent of the size of the application graph, it is said that the graph is finitely reconfigurable, FR (resp. locally reconfigurable, LR). It is shown that DR provides a natural lower bound on the time complexity of any distributed reconfiguration algorithm, and that there is no difference between being FR and LR on dynamic graphs. It is then shown that if one wishes to maintain both local reconfigurability and a fixed level of reliability, a dynamic graph must be of dimension at least one greater than the application graph. >

Error detection in arrays via dependency graphs

Abstract: This paper describes a methodology based on dependency graphs for doing concurrent run-time error detection in systolic arrays and wavefront processors. It combines the projection method of deriving systolic arrays from dependency graphs with the idea of input-triggered testing. We call the method ITRED, forInput-driven Time-Redundancy Error Detection. Tests are triggered by inserting special symbols in the input, and so the approach gives the user flexibility in trading off throughput for error coverage. Correctness of timing is proved at the dependency graph level. The method requires no extraPEs and little extra hardware. We propose several variations of the general approach and derive corresponding constraints on the modified dependency graphs that guarantee correctness. One variation performs run-time error correction using majority voting. Examples are given, including a dynamic programming algorithm, convolution, and matrix multiplication.

On the maximum likelihood estimation of rational pulse transfer-function parameters

Abstract: Thus The prediction error at anfinstant k is E(R) = 3t(k + 1)-~ (k 4-1) or E(2) = B(z)X(z)-Z X (Z) =-e-X(2) =-sY(2)

Some Experiments in VLSI Leaf-Cell Optimization,

Abstract: : This paper describes a method for local optimization of VLSI leaf cells, using the parameterized procedural layout language ALLENDE 5. Tradeoffs among delay time, power consumption, and area are illustrated. Three different implementations of the 1-bit full adder are compared: a random logic circuit, a data selector, and a PLA. The fastest random logic 1-bit full adder has a time-power product about 1/3 that of the fastest data selector, and about 1/4 that of the fastest PLA. The 4-bit parallel adder is used to illustrate the effect of loading when leaf cells are combined. (Author)

Soliton-Guided Quantum Information Processing

Abstract: We describe applications of solitons and soliton collisions to the transport, transfer, and beam-splitting of qubits carried by optical photons. The transport and transfer realize the “flying qubits” necessary for quantum information processing, and the beam-splitting leads, in theory, to an implementation of quantum computing using linear optics. These proposed applications are embedded in a uniform optical fiber and require no special device fabrication, no cooling, and no vacuum.

Numerical Optimization

Abstract: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side.

Machine Learning : A Probabilistic Perspective

Abstract: Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer

Abstract: A digital computer is generally believed to be an efficient universal computing device; that is, it is believed able to simulate any physical computing device with an increase in computation time by at most a polynomial factor. This may not be true when quantum mechanics is taken into consideration. This paper considers factoring integers and finding discrete logarithms, two problems which are generally thought to be hard on a classical computer and which have been used as the basis of several proposed cryptosystems. Efficient randomized algorithms are given for these two problems on a hypothetical quantum computer. These algorithms take a number of steps polynomial in the input size, e.g., the number of digits of the integer to be factored.

Shape matching and object recognition using shape contexts

Abstract: We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by: (1) solving for correspondences between points on the two shapes; (2) using the correspondences to estimate an aligning transform. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts, enabling us to solve for correspondences as an optimal assignment problem. Given the point correspondences, we estimate the transformation that best aligns the two shapes; regularized thin-plate splines provide a flexible class of transformation maps for this purpose. The dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning transform. We treat recognition in a nearest-neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image. Results are presented for silhouettes, trademarks, handwritten digits, and the COIL data set.

Planning Algorithms: Introductory Material

Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.