A scalable organization for distributed directories

Although directory-based cache-coherence protocols are the best choice when designing chip multiprocessors with tens of cores on-chip, the memory overhead introduced by the directory structure may not scale gracefully with the number of cores. Many approaches aimed at improving the scalability of directories have been proposed. However, they do not bring perfect scalability and usually reduce the directory memory overhead by compressing coherence information, which in turn results in extra unnecessary coherence messages and, therefore, wasted energy and some performance degradation. In this work, we present a distributed directory organization based on duplicate tags for tiled CMP architectures whose size is independent on the number of tiles of the system up to a certain number of tiles. We demonstrate that this number of tiles corresponds to the number of sets in the private caches. Additionally, we show that the area overhead of the proposed directory structure is 0.56% with respect to the on-chip data caches. Moreover, the proposed directory structure keeps the same information than a non-scalable full-map directory. Finally, we propose a mechanism that takes advantage of this directory organization to remove the network traffic caused by replacements. This mechanism reduces total traffic by 15% for a 16-core configuration compared to a traditional directory-based protocol.     

The MORPHEUS Heterogeneous Dynamically Reconfigurable Platform

Reconfigurable computing offers a wide range of low cost and efficient solutions for embedded systems. The proper choice of the reconfigurable device, the granularity of its processing elements and its memory architecture highly depend on the type of application and their data flow. Existing solutions either offer fine grain FPGAs, which rely on a hardware synthesis flow and offer the maximum degree of flexibility, or coarser grain solutions, which are usually more suitable for a particular type of data flow and applications. In this paper, we present the MORPHEUS architecture, a versatile reconfigurable heterogeneous System-on-Chip targeting streaming applications. The presented architecture exploits different reconfigurable technologies at several computation granularities that efficiently address the different applications needs. In order to efficiently exploit the presented architecture, we implemented a complete software solution to map C applications to the reconfigurable architecture. In this paper, we describe the complete toolset and provide concrete use cases of the architecture.     

Real-time massive convolution for audio applications on GPU

Massive convolution is the basic operation in multichannel acoustic signal processing. This field has experienced a major development in recent years. One reason for this has been the increase in the number of sound sources used in playback applications available to users. Another reason is the growing need to incorporate new effects and to improve the hearing experience. Massive convolution requires high computing capacity. GPUs offer the possibility of parallelizing these operations. This allows us to obtain the processing result in much shorter time and to free up CPU resources. One important aspect lies in the possibility of overlapping the transfer of data from CPU to GPU and vice versa with the computation, in order to carry out real-time applications. Thus, a synthesis of 3D sound scenes could be achieved with only a peer-to-peer music streaming environment using a simple GPU in your computer, while the CPU in the computer is being used for other tasks. Nowadays, these effects are obtained in theaters or funfairs at a very high cost, requiring a large quantity of resources. Thus, our work focuses on two mains points: to describe an efficient massive convolution implementation and to incorporate this task to real-time multichannel-sound applications.     

Comparing sensitivities of differently oriented multi-walled carbon nanotubes integrated on silicon wafer for electrochemical biosensors

In this study, we report on multi-walled carbon nanotubes fabricated on silicon substrate with four different orientations via chemical vapor deposition. It is well-known that chemical treatments improve the nanotube electrochemical reactivity by creating edge-like defects on their exposed sidewalls. Before use, we performed an acid treatment on carbon nanotubes. To prove the effect of the treatment on these nanostructured electrodes, contact angles were measured. Then, sensitivities and detection limits were evaluated performing cyclic voltammetry. Two target molecules were used: potassium ferricyanide, an inorganic electroactive molecule, and hydrogen peroxide that is a product of reactions catalyzed by many enzymes, such as oxidases and peroxidases. Carbon nanotubes with tilted tips become hydrophilic after the treatment showing a contact angle of 22° ± 2°. This kind of electrode has shown also the best electrochemical performance. Sensitivity and detection limit values are 110.0 ± 0.5 μA/(mM cm²) and 8 μM for potassium ferricyanide solutions and 16.4 ± 0.1 μA/(mM cm²) and 24 μM using hydrogen peroxide as target compound. Considering the results of wettability and voltammetric measurements, nanotubes with tilted tips-based electrodes are found to be the most promising for future biosensing applications.     

Design of high stroke electrostatic micropumps: a charge control approach with ring electrodes

A novel design strategy to avoid pull-in occurrence in electrostatic micro electro mechanical systems is proposed. It combines charge control with ring electrodes, on a circular geometry. This idea is introduced here for the design of efficient and reliable high stroke electrostatic diaphragm micropumps, while it has a broad potential applicability. A minimal lumped one degree-of-freedom model is derived and used to introduce and demonstrate the proposed approach for a circular plate geometry. Finite element models are subsequently adopted for a more detailed device modelling. As expected, charge control exhibits a stabilizing effect with respect to voltage drive, but not sufficient to achieve a full-range stability for the considered geometry. When the electrode area is properly defined, stability range can be extended up to gap closure in the central part of the membrane. In this configuration, the increase in voltage required for full-range device drive would be relevant, while in charge control the penalty is considerably lower. Finally, loading conditions and geometrical parameters for an optimized actuation are suggested.     

EDUCA: A web 2.0 authoring tool for developing adaptive and intelligent tutoring systems using a Kohonen network

This paper presents a Web 2.0 Learning Environment, for a systematic creation of adaptive and intelligent tutoring systems. Authoring contents is made by a community of users including teachers and students. The tutoring systems adapt the contents according to the best learning style using self-organizing maps (SOMs). The SOM was trained for classifying Felder–Silverman learning styles. The most important advantage of these unsupervised neural networks is that they do not require an external teacher for presenting a training set. The approach was implemented under an authoring tool that allows the production of personalized learning material to be used under collaborative and mobile learning environments. The tutoring systems together with the neural network can also be exported to mobile devices. We present different results to the approach working under the authoring tool.     

Introducing dynamic diversity into a discrete particle swarm optimization

Particle swarm optimization (PSO) is an evolutionary metaheuristic inspired by the flocking behaviour of birds, which has successfully been used to solve several kinds of problems, although there are few studies aimed at solving discrete optimization problems. One disadvantage of PSO is the risk of a premature search convergence. To prevent this, we propose to introduce diversity into a discrete PSO by adding a random velocity. The degree of the introduced diversity is not static (i.e. preset before running PSO) but instead changes dynamically according to the heterogeneity of the population (i.e. if the search has converged or not). We solve the response time variability problem (RTVP) to test these two new ideas. The RTVP is an NP-hard combinatorial scheduling problem that has recently appeared in the literature. It occurs whenever products, clients or jobs need to be sequenced in such a way that the variability in the time between the instants at which they receive the necessary resources is minimized. The most efficient algorithm for solving non-small instances of the RTVP published to date is a classical PSO algorithm, referred to by the authors as PSO-M1F. In this paper, we propose 10 discrete PSO algorithms for solving the RTVP: one based on the ideas described above (PSO  -_c3dyn$c_3\text{<italic>dyn</italic>}$) and nine based on strategies proposed in the literature and adapted for solving a discrete optimization problem such as the RTVP. We compare all 11 PSO algorithms and the computational experiment shows that, on average, the best results obtained are due to our proposal of dynamic control mechanism for introducing diversity.     

BAIS: A Bayesian Artificial Immune System for the effective handling of building blocks

Significant progress has been made in theory and design of Artificial Immune Systems (AISs) for solving hard problems accurately. However, an aspect not yet widely addressed by the research reported in the literature is the lack of ability of the AISs to deal effectively with building blocks (partial high-quality solutions coded in the antibody). The available AISs present mechanisms for evolving the population that do not take into account the relationship among the variables of the problem, potentially causing the disruption of high-quality partial solutions. This paper proposes a novel AIS with abilities to identify and properly manipulate building blocks in optimization problems. Instead of using cloning and mutation to generate new individuals, our algorithm builds a probabilistic model representing the joint probability distribution of the promising solutions and, subsequently, uses this model for sampling new solutions. The probabilistic model used is a Bayesian network due to its capability of properly capturing the most relevant interactions among the variables. Therefore, our algorithm, called Bayesian Artificial Immune System (BAIS), represents a significant attempt to improve the performance of immune-inspired algorithms when dealing with building blocks, and hence to solve efficiently hard optimization problems with complex interactions among the variables. The performance of BAIS compares favorably with that produced by contenders such as state-of-the-art Estimation of Distribution Algorithms.     

The subsequence composition of a string

Words that appear as constrained subsequences in a text-string are considered as possible indicators of the host string structure, hence also as a possible means of sequence comparison and classification. The constraint consists of imposing a bound on the number ω$\omega$ of positions in the text that may intervene between any two consecutive characters of a subsequence. A subset of such ω$\omega$-sequences is then characterized that consists, in intuitive terms, of sequences that could not be enriched with more characters without losing some occurrence in the text. A compact spatial representation is then proposed for these representative sequences, within which a number of parameters can be defined and measured. In the final part of the paper, such parameters are empirically analyzed on a small collection of text-strings endowed with various degrees of structure.     

Discrete Semantics for Hybrid Automata

Many natural systems exhibit a hybrid behavior characterized by a set of continuous laws which are switched by discrete events. Such behaviors can be described in a very natural way by a class of automata called hybrid automata. Their evolution are represented by both dynamical systems on dense domains and discrete transitions. Once a real system is modeled in a such framework, one may want to analyze it by applying automatic techniques, such as Model Checking or Abstract Interpretation. Unfortunately, the discrete/continuous evolutions not only provide hybrid automata of great flexibility, but they are also at the root of many undecidability phenomena. This paper addresses issues regarding the decidability of the reachability problem for hybrid automata (i.e., “can the system reach a state a from a state b?”) by proposing an “inaccurate” semantics. In particular, after observing that dense sets are often abstractions of real world domains, we suggest, especially in the context of biological simulation, to avoid the ability of distinguishing between values whose distance is less than a fixed ε. On the ground of the above considerations, we propose a new semantics for first-order formulæ which guarantees the decidability of reachability. We conclude providing a paradigmatic biological example showing that the new semantics mimics the real world behavior better than the precise one.