A recursive MISD architecture for pattern matching

Halaas, Arne; Svingen, B.; Nedland, Magnar; Sætrom, Pål; Snøve, Ola; Birkeland, Olaf René

doi:10.1109/tvlsi.2004.830918

Cited by 29 publications

(10 citation statements)

References 26 publications

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“…While one may argue that pipelined architectures and/or fault tolerant systems may be classified as MISD [16], it is more commonly accepted that no commercial MISD systems have yet been constructed and put in to practical use [1] though there has been research exploring the benefits of this architecture [17]. Figure 3 shows a conceptual depiction of MISD processing with n processing units and n instructions.…”

Section: Misd Mimdmentioning

confidence: 99%

“…In contrast, this thesis takes the viewpoint that a heterogeneous workload is more representative of many everyday computing needs. Other research has examined scalability from Gustafson's viewpoint [21], some even going so far as to explore it in an MISD context [17]. There are not, however, any known systems that take the approach that CD does.…”

Section: Maximum Speedupmentioning

confidence: 99%

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Building Maze Solutions with Computational Dreaming

Jackson

Paul

2017

IEEE Micro

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Modern parallel computing techniques are subject to poor scalability. Their performance tends to suffer diminishing returns and even losses with increasing parallelism. Some methods of intelligent computing, such as neural networks and genetic algorithms, lend themselves well to massively parallel systems but come with other drawbacks that can limit their usefulness such as the requirement of a training phase and/or sensitivity to randomness. This thesis investigates the feasibility of a novel method of intelligent parallel computing by implementing a true multiple instruction stream, single data stream (MISD) computing system that is theoretically nearly perfectly scalable. Computational dreaming (CD) is inspired by the structure and dreaming process of the human brain. It examines previously observed input data during a "dream phase" and is able to develop and select a simplified model to use during the day phase of computation. Using mazes as an example problem space, a CD simulator is developed and successfully used to demonstrate the viability and robustness of CD. Experiments that focused on CD viability resulted in the CD system solving 15% of mazes (ranging from small and simple to large and complex) compared with 2.2% solved by random model selection. Results also showed that approximately 50% of successful solutions generated match up with those that would be generated by algorithms such as depth first search and Dijkstra's algorithm. Experiments focusing on robustness performed repeated trials with identical parameters. Results demonstrated that CD is capable of achieving this result consistently, solving over 32% of mazes across 10 trials compared to only 3.6% solved by random model selection. A significant finding is that CD does not get stuck on local minima, always converging on a solution model. Thus, CD has the potential to enable significant contributions to computing by potentially finding elegant solutions to, for example, NP-hard or previously intractable problems.iii

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Section: Misd Mimdmentioning

confidence: 99%

Section: Maximum Speedupmentioning

confidence: 99%

Building Maze Solutions with Computational Dreaming

Jackson

Paul

2017

IEEE Micro

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“…match(a) returns 1 if the character in the position indicated by a is identical to the character it is compared with. linger (F.hit, N) is a function that if F.hit is true, F.hit will be returned for N clock-ticks (see Halaas et al [2004] for details on the linger-function). The production for W creates a sequence of N wild cards.…”

Section: Algorithm and Implementationmentioning

confidence: 99%

Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms

Sætrom¹,

Snøve²,

Sætrom³

2005

RNA

126

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We present a new microRNA target prediction algorithm called TargetBoost, and show that the algorithm is stable and identifies more true targets than do existing algorithms. TargetBoost uses machine learning on a set of validated microRNA targets in lower organisms to create weighted sequence motifs that capture the binding characteristics between microRNAs and their targets. Existing algorithms require candidates to have (1) near-perfect complementarity between microRNAs' 5 0 end and their targets; (2) relatively high thermodynamic duplex stability; (3) multiple target sites in the target's 3 0 UTR; and (4) evolutionary conservation of the target between species. Most algorithms use one of the two first requirements in a seeding step, and use the three others as filters to improve the method's specificity. The initial seeding step determines an algorithm's sensitivity and also influences its specificity. As all algorithms may add filters to increase the specificity, we propose that methods should be compared before such filtering. We show that TargetBoost's weighted sequence motif approach is favorable to using both the duplex stability and the sequence complementarity steps. (TargetBoost is available as a Web tool from http://www.interagon. com/demo/.)

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“…The PMC is a special purpose co-processor designed for locating complex patterns in unstructured data (Halaas et al, 2004). We will in the following give a brief description of its architecture and main functionality; please consult the original work (Halaas et al, 2004) for additional details.…”

Section: Search Hardwarementioning

confidence: 99%

“…Our method is based on the availability of special-purpose pattern matching hardware (Halaas et al, 2004). While this may be seen as a limitation, the new functionality that this hardware offers has, in fact, been one of the driving forces behind our work.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Rule Mining in Time Series Databases

Hetland

Sætrom²

2005

Mach Learn

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Abstract. Data mining in the form of rule discovery is a growing field of investigation. A recent addition to this field is the use of evolutionary algorithms in the mining process. While this has been used extensively in the traditional mining of relational databases, it has hardly, if at all, been used in mining sequences and time series. In this paper we describe our method for evolutionary sequence mining, using a specialized piece of hardware for rule evaluation, and show how the method can be applied to several different mining tasks, such as supervised sequence prediction, unsupervised mining of interesting rules, discovering connections between separate time series, and investigating tradeoffs between contradictory objectives by using multiobjective evolution.

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A recursive MISD architecture for pattern matching

Cited by 29 publications

References 26 publications

Building Maze Solutions with Computational Dreaming

Building Maze Solutions with Computational Dreaming

Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms

Evolutionary Rule Mining in Time Series Databases

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