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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Solving sparse triangular linear systems on parallel computers

This paper describes and compares three parallel algorithms for solving sparse triangular systems of equations. These methods involve some preprocessing overhead and are primarily of interest in solving many systems with the same coefficient matrix. The first approach is to use a fixed blocksize and form the inverse of the diagonal blocks. The second approach is to use a variable blocksize and reorder the unknowns so that the diagonal blocks are diagonal matrices. The latter technique is called level scheduling because of how it is represented in the adjacency graph, and both row-wise and jagged diagonal storage for the off-diagonal blocks are considered. These techniques are analyzed for general parallel computers and experiments are presented for the eight-processor Alliant FX/8.

Anderson, Edward↗
NASA NTRS1988

Sparse distributed memory

Theoretical models of the human brain and proposed neural-network computers are developed analytically. Chapters are devoted to the mathematical foundations, background material from computer science, the theory of idealized neurons, neurons as address decoders, and the search of memory for the best match. Consideration is given to sparse memory, distributed storage, the storage and retrieval of sequences, the construction of distributed memory, and the organization of an autonomous learning system.

Kanerva, Pentti↗
NASA NTRS1991

Improving Sparse-Aperture Interferometric Radiometry

Proposed method for designing spatial arrangement of antennas of sparse-aperture interferometric radiometry (SAIR) system increases spatial resolution of images produced by system. Technique involves cross-correlation of signals received by pairs of small antennas at different locations. Includes ring array of "flood-beam" antennas. Signals from all possible pairs of antennas cross-correlated simultaneously. Diameter of ring determines spatial resolution of image, and distribution of antennas around ring determines sampling characteristics. Conceived to improve proposed remote-sensing SAIR system placed in geosynchronous orbit; also applied to other SAIR systems intended to stare at stationary targets for relatively long times.

Ruf, Christopher S.↗
NASA NTRS1991

Distributed memory compiler design for sparse problems

A compiler and runtime support mechanism is described and demonstrated. The methods presented are capable of solving a wide range of sparse and unstructured problems in scientific computing. The compiler takes as input a FORTRAN 77 program enhanced with specifications for distributing data, and the compiler outputs a message passing program that runs on a distributed memory computer. The runtime support for this compiler is a library of primitives designed to efficiently support irregular patterns of distributed array accesses and irregular distributed array partitions. A variety of Intel iPSC/860 performance results obtained through the use of this compiler are presented.

Wu, Janet↗
NASA NTRS1990

Recognition of simple visual images using a sparse distributed memory: Some implementations and experiments

Previously, a method was described of representing a class of simple visual images so that they could be used with a Sparse Distributed Memory (SDM). Herein, two possible implementations are described of a SDM, for which these images, suitably encoded, will serve both as addresses to the memory and as data to be stored in the memory. A key feature of both implementations is that a pattern that is represented as an unordered set with a variable number of members can be used as an address to the memory. In the 1st model, an image is encoded as a 9072 bit string to be used as a read or write address; the bit string may also be used as data to be stored in the memory. Another representation, in which an image is encoded as a 256 bit string, may be used with either model as data to be stored in the memory, but not as an address. In the 2nd model, an image is not represented as a vector of fixed length to be used as an address. Instead, a rule is given for determining which memory locations are to be activated in response to an encoded image. This activation rule treats the pieces of an image as an unordered set. With this model, the memory can be simulated, based on a method of computing the approximate result of a read operation.

Jaeckel, Louis A.↗
NASA NTRS1990

The Cortex Transform as an image preprocessor for sparse distributed memory: An initial study

An experiment is described which was designed to evaluate the use of the Cortex Transform as an image processor for Sparse Distributed Memory (SDM). In the experiment, a set of images were injected with Gaussian noise, preprocessed with the Cortex Transform, and then encoded into bit patterns. The various spatial frequency bands of the Cortex Transform were encoded separately so that they could be evaluated based on their ability to properly cluster patterns belonging to the same class. The results of this study indicate that by simply encoding the low pass band of the Cortex Transform, a very suitable input representation for the SDM can be achieved.

Olshausen, Bruno↗
NASA NTRS1990

An efficient optical architecture for sparsely connected neural networks

An architecture for general-purpose optical neural network processor is presented in which the interconnections and weights are formed by directing coherent beams holographically, thereby making use of the space-bandwidth products of the recording medium for sparsely interconnected networks more efficiently that the commonly used vector-matrix multiplier, since all of the hologram area is in use. An investigation is made of the use of computer-generated holograms recorded on such updatable media as thermoplastic materials, in order to define the interconnections and weights of a neural network processor; attention is given to limits on interconnection densities, diffraction efficiencies, and weighing accuracies possible with such an updatable thin film holographic device.

Hine, Butler P., III↗
NASA NTRS1992

Sparsely Sampled Phase-Insensitive Ultrasonic Transducer Arrays

Three methods of interpretation of outputs from sparsely sampled two-dimensional array of receiving ultrasonic transducers used in transmission experiments investigated. Methods are: description of sampled beam in terms of first few spatial moments of sampled distribution of energy; use of signal-dependent cutoff to limit extent of effective receiver aperture; and use of spatial interpolation to increase apparent density of sampling during computation. Methods reduce errors in computations of shapes of ultrasonic beams.

Patrick H. Johnston↗
NASA NTRS1989

An alternative design for a sparse distributed memory

A new design for a Sparse Distributed Memory, called the selected-coordinate design, is described. As in the original design, there are a large number of memory locations, each of which may be activated by many different addresses (binary vectors) in a very large address space. Each memory location is defined by specifying ten selected coordinates (bit positions in the address vectors) and a set of corresponding assigned values, consisting of one bit for each selected coordinate. A memory location is activated by an address if, for all ten of the locations's selected coordinates, the corresponding bits in the address vector match the respective assigned value bits, regardless of the other bits in the address vector. Some comparative memory capacity and signal-to-noise ratio estimates for the both the new and original designs are given. A few possible hardware embodiments of the new design are described.

Jaeckel, Louis A.↗
NASA NTRS1989

Sparse distributed memory: Principles and operation

Sparse distributed memory is a generalized random access memory (RAM) for long (1000 bit) binary words. Such words can be written into and read from the memory, and they can also be used to address the memory. The main attribute of the memory is sensitivity to similarity, meaning that a word can be read back not only by giving the original write address but also by giving one close to it as measured by the Hamming distance between addresses. Large memories of this kind are expected to have wide use in speech recognition and scene analysis, in signal detection and verification, and in adaptive control of automated equipment, in general, in dealing with real world information in real time. The memory can be realized as a simple, massively parallel computer. Digital technology has reached a point where building large memories is becoming practical. Major design issues were resolved which were faced in building the memories. The design is described of a prototype memory with 256 bit addresses and from 8 to 128 K locations for 256 bit words. A key aspect of the design is extensive use of dynamic RAM and other standard components.

Flynn, M. J.↗
NASA NTRS1989

Sparse distributed memory

Sparse distributed memory was proposed be Pentti Kanerva as a realizable architecture that could store large patterns and retrieve them based on partial matches with patterns representing current sensory inputs. This memory exhibits behaviors, both in theory and in experiment, that resemble those previously unapproached by machines - e.g., rapid recognition of faces or odors, discovery of new connections between seemingly unrelated ideas, continuation of a sequence of events when given a cue from the middle, knowing that one doesn't know, or getting stuck with an answer on the tip of one's tongue. These behaviors are now within reach of machines that can be incorporated into the computing systems of robots capable of seeing, talking, and manipulating. Kanerva's theory is a break with the Western rationalistic tradition, allowing a new interpretation of learning and cognition that respects biology and the mysteries of individual human beings.

Denning, Peter J.↗
NASA NTRS1990

Optimal parallel solution of sparse triangular systems

A method for the parallel solution of triangular sets of equations is described that is appropriate when there are many right-handed sides. By preprocessing, the method can reduce the number of parallel steps required to solve Lx = b compared to parallel forward or backsolve. Applications are to iterative solvers with triangular preconditioners, to structural analysis, or to power systems applications, where there may be many right-handed sides (not all available a priori). The inverse of L is represented as a product of sparse triangular factors. The problem is to find a factored representation of this inverse of L with the smallest number of factors (or partitions), subject to the requirement that no new nonzero elements be created in the formation of these inverse factors. A method from an earlier reference is shown to solve this problem. This method is improved upon by constructing a permutation of the rows and columns of L that preserves triangularity and allow for the best possible such partition. A number of practical examples and algorithmic details are presented. The parallelism attainable is illustrated by means of elimination trees and clique trees.

Alvarado, Fernando L.↗
NASA NTRS1989

Learning to read aloud: A neural network approach using sparse distributed memory

An attempt to solve a problem of text-to-phoneme mapping is described which does not appear amenable to solution by use of standard algorithmic procedures. Experiments based on a model of distributed processing are also described. This model (sparse distributed memory (SDM)) can be used in an iterative supervised learning mode to solve the problem. Additional improvements aimed at obtaining better performance are suggested.

Joglekar, Umesh Dwarkanath↗
NASA NTRS1989

Some methods of encoding simple visual images for use with a sparse distributed memory, with applications to character recognition

To study the problems of encoding visual images for use with a Sparse Distributed Memory (SDM), I consider a specific class of images- those that consist of several pieces, each of which is a line segment or an arc of a circle. This class includes line drawings of characters such as letters of the alphabet. I give a method of representing a segment of an arc by five numbers in a continuous way; that is, similar arcs have similar representations. I also give methods for encoding these numbers as bit strings in an approximately continuous way. The set of possible segments and arcs may be viewed as a five-dimensional manifold M, whose structure is like a Mobious strip. An image, considered to be an unordered set of segments and arcs, is therefore represented by a set of points in M - one for each piece. I then discuss the problem of constructing a preprocessor to find the segments and arcs in these images, although a preprocessor has not been developed. I also describe a possible extension of the representation.

Jaeckel, Louis A.↗
NASA NTRS1990

An empirical investigation of sparse distributed memory using discrete speech recognition

Presented here is a step by step analysis of how the basic Sparse Distributed Memory (SDM) model can be modified to enhance its generalization capabilities for classification tasks. Data is taken from speech generated by a single talker. Experiments are used to investigate the theory of associative memories and the question of generalization from specific instances.

Danforth, Douglas G.↗
NASA NTRS1992

Kanerva's sparse distributed memory with multiple hamming thresholds

If the stored input patterns of Kanerva's Sparse Distributed Memory (SDM) are highly correlated, utilization of the storage capacity is very low compared to the case of uniformly distributed random input patterns. We consider a variation of SDM that has a better storage capacity utilization for correlated input patterns. This approach uses a separate selection threshold for each physical storage address or hard location. The selection of the hard locations for reading or writing can be done in parallel of which SDM implementations can benefit.

Pohja, Seppo↗
NASA NTRS1992

Sparse distributed memory and related models

Described here is sparse distributed memory (SDM) as a neural-net associative memory. It is characterized by two weight matrices and by a large internal dimension - the number of hidden units is much larger than the number of input or output units. The first matrix, A, is fixed and possibly random, and the second matrix, C, is modifiable. The SDM is compared and contrasted to (1) computer memory, (2) correlation-matrix memory, (3) feet-forward artificial neural network, (4) cortex of the cerebellum, (5) Marr and Albus models of the cerebellum, and (6) Albus' cerebellar model arithmetic computer (CMAC). Several variations of the basic SDM design are discussed: the selected-coordinate and hyperplane designs of Jaeckel, the pseudorandom associative neural memory of Hassoun, and SDM with real-valued input variables by Prager and Fallside. SDM research conducted mainly at the Research Institute for Advanced Computer Science (RIACS) in 1986-1991 is highlighted.

Kanerva, Pentti↗
NASA NTRS1992

Gravitational microlensing - The effect of a single lens and the impact of sparse sampling

Topics in gravitational microlensing are examined. One issue is that even in cases in which the surface mass density is quite low, microlensing events cannot be considered as caused by individual stars. Whenever a high-magnification event occurs, the quasar has crossed a caustic. These caustics, however, are complex structures even for low surface-mass densities. This is illustrated by removing a single low-mass star from an ensemble of microlenses. The magnification patterns and light curves are compared with and without this star. The differences are striking and much more complex than expected. The effect of sparse sampling on the perception of microlensed light curves is examined, and attention is given to the influence of poor time coverage on simulated light curves. A regular monitoring program for microlensing candidates (especially 2237 + 0305) with a dedicated telescope is needed to make observations in different wavebands each night.

Wambsganss, Joachim↗