Extensible sparse functional arrays with circuit parallelism

A longstanding open question in algorithms and data structures is the time and space complexity of pure functional arrays. Imperative arrays provide update and lookup operations that require constant time in the RAM theoretical model, but it is conjectured that there does not exist a RAM algorithm that achieves the same complexity for functional arrays, unless restrictions are placed on the operations. The main result of this paper is an algorithm that does achieve optimal unit time and space complexity for update and lookup on functional arrays. This algorithm does not run on a RAM, but instead it exploits the massive parallelism inherent in digital circuits. The algorithm also provides unit time operations that support storage management, as well as sparse and extensible arrays. The main idea behind the algorithm is to replace a RAM memory by a tree circuit that is more powerful than the RAM yet has the same asymptotic complexity in time (gate delays) and size (number of components). The algorithm uses an array representation that allows elements to be shared between many arrays with only a small constant factor penalty in space and time. This system exemplifies circuit parallelism, which exploits very large numbers of transistors per chip in order to speed up key algorithms. Extensible Sparse Functional Arrays (ESFA) can be used with both functional and imperative programming languages. The system comprises a set of algorithms and a circuit specification, and it has been implemented on a GPGPU with good performance

March AB, March AB1: new March tests for unlinked dynamic memory faults

Among the different types of algorithms proposed to test static random access memories (SRAMs), March tests have proven to be faster, simpler and regularly structured. New memory production technologies introduce new classes of faults usually referred to as dynamic memory faults. A few March tests for dynamic fault, with different fault coverage, have been published. In this paper, we propose new March tests targeting unlinked dynamic faults with lower complexity than published ones. Comparison results show that the proposed March tests provide the same fault coverage of the known ones, but they reduce the test complexity, and therefore the test tim

Designing a Robotic Platform for Investigating Swarm Robotics

This paper documents the design and subsequent construction of a low-cost, flexible robotic platform for swarm robotics research, and the selection of appropriate swarm algorithms for the implementation of a swarm focused predominantly on target location. The design described herein is intended to allow for the construction of robots large enough to meaningfully interact with their environment while maintaining a low per-robot cost of materials and a low assembly time. The design process is separated into three stages: mechanical design, electrical design, and software design. All major design components are described in detail under the appropriate design section. The BOM for a single robot is also included, along with relevant testing information

Automatic March tests generation for multi-port SRAMs

Testing of Multi-Port (MP) SRAMs requires special tests since the multiple and simultaneous access can sensitize faults that are different from the conventional single-port memory faults. In spite of their growing use, few works have been published on testing MP memories. In addition, most of the published work concentrated only on two ports memories (i.e., 2P memories). This paper presents a methodology to automatically generate march tests for MP memories. It is based on generations of single port memory march test firstly, then extending it to test a generic MP SRAMs. A set of experimental results shows the effectiveness of the proposed solutio

A Purely Functional Computer Algebra System Embedded in Haskell

We demonstrate how methods in Functional Programming can be used to implement a computer algebra system. As a proof-of-concept, we present the computational-algebra package. It is a computer algebra system implemented as an embedded domain-specific language in Haskell, a purely functional programming language. Utilising methods in functional programming and prominent features of Haskell, this library achieves safety, composability, and correctness at the same time. To demonstrate the advantages of our approach, we have implemented advanced Gr\"{o}bner basis algorithms, such as Faug\`{e}re's $F_4$ and $F_5$, in a composable way.Comment: 16 pages, Accepted to CASC 201

Influence of parasitic capacitance variations on 65 nm and 32 nm predictive technology model SRAM core-cells

The continuous improving of CMOS technology allows the realization of digital circuits and in particular static random access memories that, compared with previous technologies, contain an impressive number of transistors. The use of new production processes introduces a set of parasitic effects that gain more and more importance with the scaling down of the technology. In particular, even small variations of parasitic capacitances in CMOS devices are expected to become an additional source of faulty behaviors in future technologies. This paper analyzes and compares the effect of parasitic capacitance variations in a SRAM memory circuit realized with 65 nm and 32 nm predictive technology model

A Neural Model for Self Organizing Feature Detectors and Classifiers in a Network Hierarchy

Many models of early cortical processing have shown how local learning rules can produce efficient, sparse-distributed codes in which nodes have responses that are statistically independent and low probability. However, it is not known how to develop a useful hierarchical representation, containing sparse-distributed codes at each level of the hierarchy, that incorporates predictive feedback from the environment. We take a step in that direction by proposing a biologically plausible neural network model that develops receptive fields, and learns to make class predictions, with or without the help of environmental feedback. The model is a new type of predictive adaptive resonance theory network called Receptive Field ARTMAP, or RAM. RAM self organizes internal category nodes that are tuned to activity distributions in topographic input maps. Each receptive field is composed of multiple weight fields that are adapted via local, on-line learning, to form smooth receptive ftelds that reflect; the statistics of the activity distributions in the input maps. When RAM generates incorrect predictions, its vigilance is raised, amplifying subtractive inhibition and sharpening receptive fields until the error is corrected. Evaluation on several classification benchmarks shows that RAM outperforms a related (but neurally implausible) model called Gaussian ARTMAP, as well as several standard neural network and statistical classifters. A topographic version of RAM is proposed, which is capable of self organizing hierarchical representations. Topographic RAM is a model for receptive field development at any level of the cortical hierarchy, and provides explanations for a variety of perceptual learning data.Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409