VHDL Design of a Scalable VLSI Sorting Device Based on Pipelined Computation

This paper describes the VHDL design of a sorting algorithm, aiming at defining an elementary sorting unit as a building block of VLSI devices which require a huge number of sorting units. As such, care was taken to reach a reasonable low value of the area-time parameter. A sorting VLSI device, in fact, can be built as a cascade of elementary sorting units which process the input stream in a pipeline fashion: as the processing goes on, a wave of sorted numbers propagates towards the output ports. The paper describes the design starting from an initial theoretical analysis of the algorithm\u27s complexity to a VHDL behavioural analysis of the proposed architecture to a structural synthesis of a sorting block based on the Alliance tools to, finally, a silicon synthesis which was worked out again using Alliance. Two points in the proposed design are particularly noteworthy. First, the sorting architecture is suitable for treating a continuous stream of input data rather than a block of data as in many other designs. Secondly, the proposed design reaches a reasonable compromise between area and time, as it yields an A T product which compares favourably with the theoretical lower bound

A Hard Real-Time Kernel for Motorola Microcontrollers

This paper describes a real-time kernel for running embedded applications on a recent family of Motorola microcontrollers. Both periodic and aperiodic real-time tasks are managed, as well as non real-time tasks. The kernel has been called Yartos, and uses a hard real-time scheduling algorithm based on an EDF approach for the periodic task; aperiodic tasks are executed with a Total Bandwith Server

A markov-model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently

Driven by several real-life case studies and in-lab developments, synthetic memory reference generation has a long tradition in computer science research. The goal is that of reproducing the running of an arbitrary program, whose generated traces can later be used for simulations and experiments. In this paper we investigate this research context and provide principles and algorithms of a Markov-Model-based framework for supporting real-time generation of synthetic memory references effectively and efficiently. Specifically, our approach is based on a novel Machine Learning algorithm we called Hierarchical Hidden/ non Hidden Markov Model (HHnHMM). Experimental results conclude this paper

Yet Another Automated OLAP Workload Analyzer: Principles, and Experiences

n/

‎An Artificial Intelligence Framework for Supporting Coarse-Grained Workload Classification in Complex Virtual Environments

Cloud-based machine learning tools for enhanced Big Data applications}‎, ‎where the main idea is that of predicting the ``\emph{next}'' \emph{workload} occurring against the target Cloud infrastructure via an innovative \emph{ensemble-based approach} that combines the effectiveness of different well-known \emph{classifiers} in order to enhance the whole accuracy of the final classification‎, ‎which is very relevant at now in the specific context of \emph{Big Data}‎. ‎The so-called \emph{workload categorization problem} plays a critical role in improving the efficiency and reliability of Cloud-based big data applications‎. ‎Implementation-wise‎, ‎our method proposes deploying Cloud entities that participate in the distributed classification approach on top of \emph{virtual machines}‎, ‎which represent classical ``commodity'' settings for Cloud-based big data applications‎. ‎Given a number of known reference workloads‎, ‎and an unknown workload‎, ‎in this paper we deal with the problem of finding the reference workload which is most similar to the unknown one‎. ‎The depicted scenario turns out to be useful in a plethora of modern information system applications‎. ‎We name this problem as \emph{coarse-grained workload classification}‎, ‎because‎, ‎instead of characterizing the unknown workload in terms of finer behaviors‎, ‎such as CPU‎, ‎memory‎, ‎disk‎, ‎or network intensive patterns‎, ‎we classify the whole unknown workload as one of the (possible) reference workloads‎. ‎Reference workloads represent a category of workloads that are relevant in a given applicative environment‎. ‎In particular‎, ‎we focus our attention on the classification problem described above in the special case represented by \emph{virtualized environments}‎. ‎Today‎, ‎\emph{Virtual Machines} (VMs) have become very popular because they offer important advantages to modern computing environments such as cloud computing or server farms‎. ‎In virtualization frameworks‎, ‎workload classification is very useful for accounting‎, ‎security reasons‎, ‎or user profiling‎. ‎Hence‎, ‎our research makes more sense in such environments‎, ‎and it turns out to be very useful in a special context like Cloud Computing‎, ‎which is emerging now‎. ‎In this respect‎, ‎our approach consists of running several machine learning-based classifiers of different workload models‎, ‎and then deriving the best classifier produced by the \emph{Dempster-Shafer Fusion}‎, ‎in order to magnify the accuracy of the final classification‎. ‎Experimental assessment and analysis clearly confirm the benefits derived from our classification framework‎. ‎The running programs which produce unknown workloads to be classified are treated in a similar way‎. ‎A fundamental aspect of this paper concerns the successful use of data fusion in workload classification‎. ‎Different types of metrics are in fact fused together using the Dempster-Shafer theory of evidence combination‎, ‎giving a classification accuracy of slightly less than $80\%$‎. ‎The acquisition of data from the running process‎, ‎the pre-processing algorithms‎, ‎and the workload classification are described in detail‎. ‎Various classical algorithms have been used for classification to classify the workloads‎, ‎and the results are compared‎

Method of and arrangement for distinguishing between voiced and unvoiced speech elements

In distinguishing between voiced and unvoiced speech elements use is made of the fact that the spectra of voiced sounds lie predominantly at or below about 1 kHz, and the spectra of unvoiced sounds lie predominantly at or above about 2 kHz. A change from a voiced sound to an unvoiced sound or vice versa always produces a clear shift of the spectrum, and that without such a change, there is no such clear shift. From the lower- and higher-frequency energy components, a measure of the location of the spectral centroid is derived which is used for a first decision. Based on the difference between two successive measures, a second decision is made by which the first can be corrected

VHDL design and simulation of a pipelined scalable architecture for high speed sorting

In this paper, we present a novel sorting algorithm which works trough a cascade of pipelined sorting units. The sorting device has been simulated in VHDL both at a behaviour level and at a gate level. The results of the simulation are shown

Automatic formant estimation and tracking with a genetic algorithm

An algorithm to perform formant estimation and tracking is described in this paper. It is based on a least squared minimization of a weighted euclidean distance with a genetic algorithm. It is shown that the algorithm performs a good estimation of the formants and that their tracking is achieved. Some experimental results are shown