Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 10,000 papers for “Distributed memory”

Memory, Distributed

Leon N. Cooper · Birkhäuser Boston eBooks · 1989

That most intriguing aspect of human memory, is its persistence in spite of continual loss of individual neurons over the lifetime of the individual, has led many workers to the concept of distributed memory. For distributed memory (more like a hologram than a photograph) possesses in a very natural way the property of relative invulnerability to the loss of storage units: individual memory sites hold superimposed information concerning many events. In order to obtain a single event, information must be gathered from many sites. Loss of individual units decreases signal-to-noise ratios but does not lose items of information.KeywordsFiring RateNeural ActivityIntrigue AspectSynaptic JunctionAverage Firing RateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Cited by 0

Distributed memory

Jon M. Slack · 1986

The properties of distributed representations and memory systems are explored as a potential basis for non-deterministic parsing mechanisms. The structure of a distributed chart parsing representation is outlined. Such a representation encodes both immediate-dominance and terminal projection information on a single composite memory vector. A parsing architecture is described which uses a permanent store of context-free rule patterns encoded as split composite vectors, and two interacting working memory units. These latter two units encode vectors which correspond to the active and inactive edges of an active chart parsing scheme. This type of virtual parsing mechanism is compatible with both a macro-level implementation based on standard sequential processing and a micro-level implementation using a massively parallel architecture.

Cited by 4Open access

Distributed Shared Memory

Alan Judge, Patrick Nixon, Brendan Tangney et al. · eCite Digital Repository (University of Tasmania) · 1999

Cited by 4Open access

Distributed associative memory

Philip Sterne · Apollo (University of Cambridge) · 2011

This dissertation modifies error-correcting codes and Bloom filters to create high-capacity associative memories. These associative memories use principled statistical inference and are distributed as no single component contains enough information to complete the task by itself, yet the components can collectively solve the task by passing information to each other. These associative memories are also robust to hardware failure as their distributed nature ensures there is no single point of failure. This dissertation starts by simplifying a Bloom filter so that it tolerates hardware failure (albeit with reduced performance). An efficient associative memory is created by performing inference over the set of items stored in the Bloom filter. This architecture suggests a modification which forgets old patterns stored in the associative memory (known as a palimpsest memory). It is shown that overwriting old patterns in an independent manner reduces performance, but is still comparable to the well-known Hopfield network. The lost performance can be regained using integer storage which allows the superposition of the pattern representation, or ensuring bits are not overwritten independently using concepts from errorcorrecting codes. The final task performs recall in continuous time using components which are more similar to neurons than used in the rest of the dissertation. The resulting memory has the exciting ability to recall many patterns simultaneously. Statistical inference ensures gradual degradation of the performance as an associative memory is overloaded. Since many definitions of associative memory capacity rely on the existence of catastrophic failure a new definition of capacity is provided. In spite of some biologically unrealistic attributes, this work is relevant to the understanding of the brain as it provides high performance solutions to the associative memory task which is known to be relevant to the brain.

Cited by 2Open access

Distributed Shared Memory

Ratan K. Ghosh, Hiranmay Ghosh · 2023

A many-core distributed system consists of multiple multi-core node clusters connected via network on chips (NoCs). Scaling up performance on a many-core system requires careful partitioning and placement of data to extract maximum parallelism. Furthermore, there are technological innovations with specialized hardware which accelerate performance. These include graphics processing unit (GPU), field programmable gateway arrays (FPGAs), and machine/deep learning (ML/DL) accelerators like Google's tensor processing unit (TPU). Simultaneously, intensive research on programming newer architecture led to the discovery of diverse computing abstractions such as MapReduce, Spark, and Dryad for data-parallel applications on these systems. These days, large-scale data-intensive applications like page rank, placement of ads, and social networks are implemented using many-core distributed systems. There is no single abstraction that fits all data-parallel applications. As a result, the applications performing well in one model perform poorly in another. The programmers find the context switching from one abstraction to another difficult, and their productivity suffers. Software distributed shared memory (S-DSM) research saw a resurgence with newer computing platforms. From a programmer's perspective, the shared memory programming model is a natural extension of the uniprocessor memory model on a distributed system. S-DSM implementation is transparent to the programmer. So, it allows the programmers to handle synchronizations in the familiar shared memory model. Their productivity is unhindered by the complexity of message-passing. The biggest challenge in S-DSM implementation is maintaining the consistency of shared variables. Inconsistent values of shared variables lead to a program's behavior being different from the programmer's expectations.

Cited by 5

Distributed Shared Memory

Changhun Lee · 2002

Practical parallel machines which are parallel virtual machine (PVP), symmetric multiprocessor (SMP), massively parallel processor (MPP), distributed shared memory (DSM), and clusters of workstation (COWs) are described. According to the classification of practical parallel machines, this paper mainly deals with distributed shared memory (DSM). The concept of DSM is explained, and the comparison among software, hardware, and hybrid DSM implementations are presented.

Cited by 0

Sparse Distributed Memory

Pattern Data Analysis, František Grebeníček · 2000

:#7KLV#SDSHU#GLVFXVVHV##KRZ#VRPH#VWDWLVWLFDO#SURSHUWLHV#RI#SDWWHUQ#GDWD#FDQ#DIIHFW# HIILFLHQF\\#RI#.DQHUYD#V#6SDUVH#'LVWULEXWHG#0HPRU\\##6'0###7KHQ##LW#VXJJHVWV#DQ#PHWKRG# ZKLFK#VKRXOG#LPSURYH#6'0#HIILFLHQF\\##7KH#PHWKRG#ORRNV#IRU#RSWLPDO#6'0#SDUDPHWHUV# DFFRUGLQJ#WR#SURSHUWLHV#RI#SDWWHUQ#GDWD##5HVXOWV#RI#VLPSOH#H[SHULPHQW#DUH#LQFOXGHG## Key Words:#6SDUVH#'LVWULEXWHG#0HPRU\\##QHXUDO#QHWZRUN##SDWWHUQ#UHFRJQLWLRQ# 1 Introduction 7KH#6'0#ZDV#GHYHORSHG#E\\#.DQHUYD#>#@#DQG#LW#PD\\#EH#UHJDUGHG#HLWKHU#DV#DQ#H[WHQVLRQ#RI#D# FODVVLFDO#UDQGRP#DFFHVV#PHPRU\\##5$0##RU#DV#D#VSHFLDO#W\\SH#RI#WKUHH#OD\\HU#IHHGIRUZDUG#QHXUDO# QHWZRUN##7KH#PDLQ#6'0#DOWHUDWLRQV#WR#WKH#5$0#DUH## . 7KH 6'0 FDOFXODWHV +DPPLQJ GLVWDQFHV EHWZHHQ WKH UHIHUHQFH DGGUHVV DQG HDFK ORFDWLRQ DGGUHVV# )RU HDFK GLVWDQFH ZKLFK LV OHVV RU HTXDO WR WKH JLYHQ UDGLXV WKH FRUUHVSRQGLQJ ORFDWLRQ LV VHOHFWHG# . 7KH RZQ PHPRU\\ LV UHSUHVHQWHG E\\ m n FRXQWHUV #ZKHUH n LV QXPEHU RI ORFDWLRQV DQG m LV WKH LQSXW GDWD OHQJWK# LQVWHDG RI VLQJOH#EL...

Cited by 0

Sparse Distributed Memory

Tim A. Hely · Encyclopedia of Cognitive Science · 2005

Abstract The sparse distributed memory is a mathematical technique for storing and retrieving large random binary patterns. The memory has been used in simple visual and linguistic applications, and has been proposed as a model of cerebellum function.

Cited by 15

Distributed-Memory Machines*

Edgar T. Kalns, Hong Xu, Lionel M. Nz · 1993

Determining an appropriate data distribution among different memories is critical to the performance of data-parallel programs on distributedmemory machines. By analyzing the computational load of data arrays and the communication cornplexity of various data movement operations in a program, this paper suggests a first-order cost model for determining a small set of appropriate data distribution patterns among many possible choices. A new data distribution specification, name! y CYBLOCK, is proposed to enhance the expressiveness of data distribution specifications being proposed in High Performance Fortran. Cost analysis of two case studies: a linear system solver and a Purdue-set benchmark loop, are used to illustrate the proposed evaluation method. The model correctly predicts the relative performance of the case studies when implemented with various regular data distributions on an nCUBE2 multicomputer.

Cited by 0

Coordinating Distributed Memory

Kristin Marie Bivens, Kelli Cargile Cook · Journal of Business and Technical Communication · 2018

This case study of an environmental engineer’s proposal-writing process reveals how the engineer (Beatrice) reifies, archives, and accesses her distributed memory across physical and digital sources in order to write proposals. Based on the authors’ observations of Beatrice’s proposal-writing process and their interviews with her, they arrived at three key conclusions: Beatrice distributes her memory across multiple physical and digital sources, the (spreadsheet) product calculator helps Beatrice to manage her cognitive load and relieve her working memory, and the product calculator allows Beatrice to reassemble her distributed memory and coordinate her cognition.

Cited by 3

Distributed shared memory

John K. Bennett, J. B. Carter, Anna L. Cox et al. · 1992

Distributed shared memory (DSM) is the provision in software of a shared memory programming model on a distributed memory machine [6]. We are exploring the use of DSM in a cluster-based computing environment of workstations and servers connected by a local internetwork.We have concentrated so far on using DSM to program compute-intensive tasks on networks of workstations. For these applications, DSM is a superior programming model compared to message passing, because it relieves the programmer from having to worry about data motion. Second, DSM allows applications written for shared memory machines to be ported with relative ease to distributed memory machines. Finally, DSM offers a natural paradigm for integrating both the locally shared memory and the globally distributed memory of anticipated future networks of shared memory multiprocessors. In particular, in such an environment, local interprocess communication is likely to dominate and therefore should be implemented in the most efficient form possible, i.e., by using the (hardware) shared memory available. It then follows that to achieve a single paradigm for both local and remote interprocess communication, DSM is the natural choice.

Cited by 3Open access

Sparse Distributed Memory

Pentti Kanerva · Medical Entomology and Zoology · 1988

From the Publisher: Motivated by the remarkable fluidity of memory the way in which items are pulled spontaneously and effortlessly from our memory by vague similarities to what is currently occupying our attention Sparse Distributed Memory presents a mathematically elegant theory of human long term memory. The book, which is self contained, begins with background material from mathematics, computers, and neurophysiology; this is followed by a step by step development of the memory model. The concluding chapter describes an autonomous system that builds from experience an internal model of the world and bases its operation on that internal model. Close attention is paid to the engineering of the memory, including comparisons to ordinary computer memories. Sparse Distributed Memory provides an overall perspective on neural systems. The model it describes can aid in understanding human memory and learning, and a system based on it sheds light on outstanding problems in philosophy and artificial intelligence. Applications of the memory are expected to be found in the creation of adaptive systems for signal processing, speech, vision, motor control, and (in general) robots. Perhaps the most exciting aspect of the memory, in its implications for research in neural networks, is that its realization with neuronlike components resembles the cortex of the cerebellum. Pentti Kanerva is a scientist at the Research Institute for Advanced Computer Science at the NASA Ames Research Center and a visiting scholar at the Stanford Center for the Study of Language and Information. A Bradford Book.

Cited by 973