The Experts below are selected from a list of 3312 Experts worldwide ranked by ideXlab platform

Florin Balasa - One of the best experts on this subject based on the ideXlab platform.

  • Energy-aware memory management for embedded Multidimensional Signal Processing applications
    Eurasip Journal on Embedded Systems, 2017
    Co-Authors: Florin Balasa, Noha Abuaesh, Cristian V. Gingu, Ilie I Luican, Hongwei Zhu
    Abstract:

    In real-time data-intensive multimedia Processing applications, data transfer and storage significantly influence, if not dominate, all the major cost parameters of the design space—namely energy consumption, performance, and chip area. This paper presents an electronic design automation (EDA) methodology for the high-level design of hierarchical memory architectures in embedded data-intensive applications, mainly in the area of Multidimensional Signal Processing. Different from the previous works, the problems of data assignment to the memory layers, of mapping the Signals into the physical memories, and of banking the on-chip memory are addressed in a consistent way, based on the same formal model. This memory management framework employs techniques specific to the integral polyhedra based dependence analysis. The main design target is the reduction of the static and dynamic energy consumption in the hierarchical memory subsystem.

  • multithreaded Signal to memory mapping algorithm for embedded Multidimensional Signal Processing
    International Conference on Control Systems and Computer Science, 2015
    Co-Authors: Ayah Helal, Florin Balasa
    Abstract:

    Many Signal Processing systems, particularly in the multimedia and telecommunication domains, are synthesized to execute data-dominated applications. Their behavior is described in a high-level programming language, where the code is typically organized in sequences of loop nests and the main data structures are Multidimensional arrays. This paper proposes a memory management algorithm for mapping Multidimensional Signals (arrays) to physical memory blocs. The advantages of this novel technique are the following: (a) it can be applied to multilayer memory hierarchies, which makes it particularly useful in embedded systems design, (b) it provides metrics of quality for the overall memory allocation solution: the minimum data storage of each Multidimensional Signal in the behavioral specification (therefore, the optimal memory sharing between the elements of same arrays), as well as the minimum data storage for the entire specification (therefore, the optimal memory sharing between all the array elements and scalars in the code), (c) it is well-suited to a dynamic multithreading implementation, which makes it computationally efficient.

  • optimization of memory banking in embedded Multidimensional Signal Processing applications
    International Symposium on Circuits and Systems, 2015
    Co-Authors: Florin Balasa, Noha Abuaesh, Cristian V. Gingu, Hongwei Zhu
    Abstract:

    Hierarchical memory organizations are used in embedded systems to reduce energy consumption and improve performance by assigning the frequently-accessed data to the low levels of memory hierarchy. Within a given level of hierarchy, energy and access times can be further reduced by memory banking. This paper addresses the problem of banking optimization, presenting a dynamic programming approach that takes into account all three major design objectives — energy consumption, performance, and die area, letting the designers decide on their relative importance for a specific project. The time complexity is independent of the size of the storage access trace and of the memory size — a significant advantage in terms of computation speed when these two parameters are large.

  • ISCAS - Optimization of memory banking in embedded Multidimensional Signal Processing applications
    2015 IEEE International Symposium on Circuits and Systems (ISCAS), 2015
    Co-Authors: Florin Balasa, Noha Abuaesh, Cristian V. Gingu, Hongwei Zhu
    Abstract:

    Hierarchical memory organizations are used in embedded systems to reduce energy consumption and improve performance by assigning the frequently-accessed data to the low levels of memory hierarchy. Within a given level of hierarchy, energy and access times can be further reduced by memory banking. This paper addresses the problem of banking optimization, presenting a dynamic programming approach that takes into account all three major design objectives — energy consumption, performance, and die area, letting the designers decide on their relative importance for a specific project. The time complexity is independent of the size of the storage access trace and of the memory size — a significant advantage in terms of computation speed when these two parameters are large.

  • CSCS - Multithreaded Signal-to-Memory Mapping Algorithm for Embedded Multidimensional Signal Processing
    2015 20th International Conference on Control Systems and Computer Science, 2015
    Co-Authors: Ayah Helal, Florin Balasa
    Abstract:

    Many Signal Processing systems, particularly in the multimedia and telecommunication domains, are synthesized to execute data-dominated applications. Their behavior is described in a high-level programming language, where the code is typically organized in sequences of loop nests and the main data structures are Multidimensional arrays. This paper proposes a memory management algorithm for mapping Multidimensional Signals (arrays) to physical memory blocs. The advantages of this novel technique are the following: (a) it can be applied to multilayer memory hierarchies, which makes it particularly useful in embedded systems design, (b) it provides metrics of quality for the overall memory allocation solution: the minimum data storage of each Multidimensional Signal in the behavioral specification (therefore, the optimal memory sharing between the elements of same arrays), as well as the minimum data storage for the entire specification (therefore, the optimal memory sharing between all the array elements and scalars in the code), (c) it is well-suited to a dynamic multithreading implementation, which makes it computationally efficient.

S. Bourennane - One of the best experts on this subject based on the ideXlab platform.

  • Multidimensional Signal Processing and applications.
    TheScientificWorldJournal, 2014
    Co-Authors: S. Bourennane, Julien Marot, Caroline Fossati, Ahmed Bouridane, Klaus Spinnler
    Abstract:

    In our daily lives and almost unconsciously, we deal with Multidimensional data. From color images converted to the luminance and chrominance format to magnetic resonance images commonly acquired for health purposes, from different fashions to write an alphabet to array Processing Signals underlying any telecommunication system, we deal with Multidimensional data. In this special issue, we tried to show the variety of the topics which are currently investigated with Multidimensional Signal Processing tools. The mathematical tools presented in this issue are as diverse as adaptive detectors, wavelet Processing, principal component analysis, and improved classical image Processing tools such as histogram equalization. In the array Processing paradigm, a two-dimensional matrix containing the data depends on the polarization properties of the sources, their number, and the number of sensors in the receiving antenna. Hence the interest of a Multidimensional representation, including a polarization variable with two or three possible values, and a real and a complex part for the source amplitudes. In the image Processing paradigm, data are as various as magnetic resonance or color images, whose representation can be transferred from the RGB (red green blue) format to other spaces emphasizing for instance the luminance or the chrominance. It is shown how magnetic resonance brain images are classified with support vector machine. To avoid problems related to high dimensionality, which is current in big data Processing, adequate features are extracted from the data by discrete wavelet transform and principal component analysis. Color spaces, which are useful for skin detection, for instance, are also further investigated: whatever the representation space is, a color image is a third order tensor, in other words, a three-dimensional data. It is shown how to detect image splicing with the help of merged features in the chrominance space: the relationships between pixels in a neighborhood are studied with a Markov process and the extraction of DCT features from the chrominance channel. Then, with the help of new color spaces, it is shown how evolved versions of neural networks called extreme learning machines can fuse multiple information such as color and local spatial information from face images. The “multi” aspect can also appear in the image Processing paradigm when multiple images are obtained from several parameters. In images provided by synthetic aperture radar exploited for flood detection, contrast enhancement is achieved by an adjustable histogram equalization technique. For such an application where the visual aspect of the results are much important much, a color image, that is, a Multidimensional Signal, can be built from several two-dimensional result images, to get an informative map, where the color informs on the nature of the imaged scene, flooded or not, for instance. Starting from images, a set of Multidimensional data is extracted from Serbian texts: the Serbian alphabet, made of 30 letters, can be expressed in a Latin or in a Cyrillic fashion. All letters can be classified into four sets. By studying the frequencies of occurrence of each type of letter in a text, one can deduce that this text is written in the Latin or the Cyrillic fashion. In this application, matrices describing the cooccurrence in the distribution of the four types of letters are built out of any text, to make use of the classical texture features. Adapting the texture features to such a text recognition application, introducing a parameter which is the writing fashion, is a brand new idea. The “multi” aspect can also relate to multiresolution. Histogram of oriented gradients and hue descriptors can be merged to combine information related to the shape of an object and its color. By computing the merged data at several resolution levels, an innovative Multidimensional descriptor is obtained. An application considered in this special issue is aircraft characterization and detection of images. In a nutshell, the “multi” representation attracts the interest of researchers from very diverse application fields. Hopefully, this special issue will contribute in diffusing the models and tools of Multidimensional Signal Processing to various application fields. Salah Bourennane Julien Marot Caroline Fossati Ahmed Bouridane Klaus Spinnler

  • Multidimensional Signal Processing using lower-rank tensor approximation
    2003 IEEE International Conference on Acoustics Speech and Signal Processing 2003. Proceedings. (ICASSP '03)., 2003
    Co-Authors: D. Muti, S. Bourennane
    Abstract:

    The paper presents a new fast optimal lower rank tensor approximation (FOLRTA) method for lower rank-(R/sub 1/,..., R/sub N/) tensor approximation applied to Multidimensional Signal Processing. It is founded on a new approach which consists of considering Multidimensional data as global tensors instead of splitting them into matrices or vectors for later classical second order array Processing. Its basic principle is to project the initial data tensor into the Signal subspace, in each consecutive mode. The developed method is the first analytical solution to the Tucker3 tensor decomposition. We show in a simple example of noise reduction of a color image the efficiency of this method. It can also be applied in seismic, acoustics or multimedia Signal Processing.

  • ICASSP (3) - Multidimensional Signal Processing using lower-rank tensor approximation
    2003 IEEE International Conference on Acoustics Speech and Signal Processing 2003. Proceedings. (ICASSP '03)., 1
    Co-Authors: D. Muti, S. Bourennane
    Abstract:

    The paper presents a new fast optimal lower rank tensor approximation (FOLRTA) method for lower rank-(R/sub 1/,..., R/sub N/) tensor approximation applied to Multidimensional Signal Processing. It is founded on a new approach which consists of considering Multidimensional data as global tensors instead of splitting them into matrices or vectors for later classical second order array Processing. Its basic principle is to project the initial data tensor into the Signal subspace, in each consecutive mode. The developed method is the first analytical solution to the Tucker3 tensor decomposition. We show in a simple example of noise reduction of a color image the efficiency of this method. It can also be applied in seismic, acoustics or multimedia Signal Processing.

Doru V Nasui - One of the best experts on this subject based on the ideXlab platform.

  • leakage aware scratch pad memory banking for embedded Multidimensional Signal Processing
    International Conference on Acoustics Speech and Signal Processing, 2014
    Co-Authors: Florin Balasa, Noha Abuaesh, Cristian V. Gingu, Doru V Nasui
    Abstract:

    Partitioning a memory into multiple banks that can be independently accessed is an approach mainly used for the reduction of the dynamic energy consumption. When leakage energy comes into play as well, the idle memory banks must be put in a low-leakage `dormant' state to save static energy when not accessed. The energy savings must be large enough to compensate the energy overhead spent by changing the bank status from active to dormant, then back to active again. This paper addresses the problem of energy-aware on-chip memory banking, taking into account - during the exploration of the search space - the idleness time intervals of the data mapped into the memory banks. As on-chip storage, we target scratch-pad memories (SPMs) since they are commonly used in embedded systems as an alternative to caches. The proposed approach proved to be computationally fast and very efficient when tested for several data-intensive applications, whose behavioral specifications contain Multidimensional arrays as main data structures.

  • ICASSP - Leakage-aware scratch-pad memory banking for embedded Multidimensional Signal Processing
    2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014
    Co-Authors: Florin Balasa, Noha Abuaesh, Cristian V. Gingu, Doru V Nasui
    Abstract:

    Partitioning a memory into multiple banks that can be independently accessed is an approach mainly used for the reduction of the dynamic energy consumption. When leakage energy comes into play as well, the idle memory banks must be put in a low-leakage `dormant' state to save static energy when not accessed. The energy savings must be large enough to compensate the energy overhead spent by changing the bank status from active to dormant, then back to active again. This paper addresses the problem of energy-aware on-chip memory banking, taking into account - during the exploration of the search space - the idleness time intervals of the data mapped into the memory banks. As on-chip storage, we target scratch-pad memories (SPMs) since they are commonly used in embedded systems as an alternative to caches. The proposed approach proved to be computationally fast and very efficient when tested for several data-intensive applications, whose behavioral specifications contain Multidimensional arrays as main data structures.

  • High-quality data assignment to hierarchical memory organizations for Multidimensional Signal Processing
    Fifth Asia Symposium on Quality Electronic Design (ASQED 2013), 2013
    Co-Authors: Florin Balasa, Ilie I Luican, Doru V Nasui
    Abstract:

    In real-time data-dominated communication and multimedia Processing applications, data transfer and storage significantly influence, if not dominate, all the major cost parameters of the design space - namely power consumption, performance, and chip area. Multi-layer memory hierarchies are used to reduce the energy consumption, but also to enhance the system performance. The energy-aware optimization of a hierarchical memory architecture implies the addition of layers of smaller and faster memories used to store the intensely-used data, in order to better exploit the non-uniform memory accesses. This paper presents an electronic design automation (EDA) methodology for energy-efficient Signal assignment to the memory layers of a hierarchical storage organization. This approach starts from the behavioral specification of a given application and, employing algebraic techniques specific to the data-dependence analysis used in modern compilers, identifies those parts of (Multidimensional) arrays intensely accessed. Tested on a two-layer memory hierarchy, this EDA methodology led to savings of storage energy consumption from 40 % to over 60 % relative to the energy used in the case of flat memory designs.

  • Signal assignment model for the memory management of Multidimensional Signal Processing applications
    Signal Processing Systems, 2011
    Co-Authors: Florin Balasa, Ilie I Luican, Doru V Nasui
    Abstract:

    Many Signal Processing systems, particularly in the multimedia and telecom domains, are synthesized to execute data-dominated applications. Their behavior is described in a high-level programming language, where the code is typically organized in sequences of loop nests and the main data structures are Multidimensional arrays. Since data transfer and storage have a significant impact on both the system performance and the major cost parameters--power consumption and chip area, the designer must spend a significant effort during the system development process on the exploration of the memory subsystem in order to achieve a cost-optimized design. This paper presents a memory allocation methodology for Multidimensional Signal Processing applications, focusing on the problem of efficiently mapping the Multidimensional Signals from the algorithmic specification into the physical memory. In a first phase, two previous mapping models are implemented within a common theoretical framework, which is advantageous from both the point of view of computational efficiency and the amount of allocated data storage. Different from all the previous mapping models that aim to optimize the memory sharing between the elements of a same array (creating separate windows in the physical memory for distinct arrays), this proposed mapping model exploit--in a second phase--the possibility of memory sharing between the elements of different arrays. As a consequence, this Signal assignment approach yields significant savings in the amount of data storage resulted after mapping.

D. Muti - One of the best experts on this subject based on the ideXlab platform.

  • Multidimensional Signal Processing using lower-rank tensor approximation
    2003 IEEE International Conference on Acoustics Speech and Signal Processing 2003. Proceedings. (ICASSP '03)., 2003
    Co-Authors: D. Muti, S. Bourennane
    Abstract:

    The paper presents a new fast optimal lower rank tensor approximation (FOLRTA) method for lower rank-(R/sub 1/,..., R/sub N/) tensor approximation applied to Multidimensional Signal Processing. It is founded on a new approach which consists of considering Multidimensional data as global tensors instead of splitting them into matrices or vectors for later classical second order array Processing. Its basic principle is to project the initial data tensor into the Signal subspace, in each consecutive mode. The developed method is the first analytical solution to the Tucker3 tensor decomposition. We show in a simple example of noise reduction of a color image the efficiency of this method. It can also be applied in seismic, acoustics or multimedia Signal Processing.

  • ICASSP (3) - Multidimensional Signal Processing using lower-rank tensor approximation
    2003 IEEE International Conference on Acoustics Speech and Signal Processing 2003. Proceedings. (ICASSP '03)., 1
    Co-Authors: D. Muti, S. Bourennane
    Abstract:

    The paper presents a new fast optimal lower rank tensor approximation (FOLRTA) method for lower rank-(R/sub 1/,..., R/sub N/) tensor approximation applied to Multidimensional Signal Processing. It is founded on a new approach which consists of considering Multidimensional data as global tensors instead of splitting them into matrices or vectors for later classical second order array Processing. Its basic principle is to project the initial data tensor into the Signal subspace, in each consecutive mode. The developed method is the first analytical solution to the Tucker3 tensor decomposition. We show in a simple example of noise reduction of a color image the efficiency of this method. It can also be applied in seismic, acoustics or multimedia Signal Processing.

Noha Abuaesh - One of the best experts on this subject based on the ideXlab platform.

  • Energy-aware memory management for embedded Multidimensional Signal Processing applications
    Eurasip Journal on Embedded Systems, 2017
    Co-Authors: Florin Balasa, Noha Abuaesh, Cristian V. Gingu, Ilie I Luican, Hongwei Zhu
    Abstract:

    In real-time data-intensive multimedia Processing applications, data transfer and storage significantly influence, if not dominate, all the major cost parameters of the design space—namely energy consumption, performance, and chip area. This paper presents an electronic design automation (EDA) methodology for the high-level design of hierarchical memory architectures in embedded data-intensive applications, mainly in the area of Multidimensional Signal Processing. Different from the previous works, the problems of data assignment to the memory layers, of mapping the Signals into the physical memories, and of banking the on-chip memory are addressed in a consistent way, based on the same formal model. This memory management framework employs techniques specific to the integral polyhedra based dependence analysis. The main design target is the reduction of the static and dynamic energy consumption in the hierarchical memory subsystem.

  • optimization of memory banking in embedded Multidimensional Signal Processing applications
    International Symposium on Circuits and Systems, 2015
    Co-Authors: Florin Balasa, Noha Abuaesh, Cristian V. Gingu, Hongwei Zhu
    Abstract:

    Hierarchical memory organizations are used in embedded systems to reduce energy consumption and improve performance by assigning the frequently-accessed data to the low levels of memory hierarchy. Within a given level of hierarchy, energy and access times can be further reduced by memory banking. This paper addresses the problem of banking optimization, presenting a dynamic programming approach that takes into account all three major design objectives — energy consumption, performance, and die area, letting the designers decide on their relative importance for a specific project. The time complexity is independent of the size of the storage access trace and of the memory size — a significant advantage in terms of computation speed when these two parameters are large.

  • ISCAS - Optimization of memory banking in embedded Multidimensional Signal Processing applications
    2015 IEEE International Symposium on Circuits and Systems (ISCAS), 2015
    Co-Authors: Florin Balasa, Noha Abuaesh, Cristian V. Gingu, Hongwei Zhu
    Abstract:

    Hierarchical memory organizations are used in embedded systems to reduce energy consumption and improve performance by assigning the frequently-accessed data to the low levels of memory hierarchy. Within a given level of hierarchy, energy and access times can be further reduced by memory banking. This paper addresses the problem of banking optimization, presenting a dynamic programming approach that takes into account all three major design objectives — energy consumption, performance, and die area, letting the designers decide on their relative importance for a specific project. The time complexity is independent of the size of the storage access trace and of the memory size — a significant advantage in terms of computation speed when these two parameters are large.

  • leakage aware scratch pad memory banking for embedded Multidimensional Signal Processing
    International Conference on Acoustics Speech and Signal Processing, 2014
    Co-Authors: Florin Balasa, Noha Abuaesh, Cristian V. Gingu, Doru V Nasui
    Abstract:

    Partitioning a memory into multiple banks that can be independently accessed is an approach mainly used for the reduction of the dynamic energy consumption. When leakage energy comes into play as well, the idle memory banks must be put in a low-leakage `dormant' state to save static energy when not accessed. The energy savings must be large enough to compensate the energy overhead spent by changing the bank status from active to dormant, then back to active again. This paper addresses the problem of energy-aware on-chip memory banking, taking into account - during the exploration of the search space - the idleness time intervals of the data mapped into the memory banks. As on-chip storage, we target scratch-pad memories (SPMs) since they are commonly used in embedded systems as an alternative to caches. The proposed approach proved to be computationally fast and very efficient when tested for several data-intensive applications, whose behavioral specifications contain Multidimensional arrays as main data structures.

  • ICASSP - Leakage-aware scratch-pad memory banking for embedded Multidimensional Signal Processing
    2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014
    Co-Authors: Florin Balasa, Noha Abuaesh, Cristian V. Gingu, Doru V Nasui
    Abstract:

    Partitioning a memory into multiple banks that can be independently accessed is an approach mainly used for the reduction of the dynamic energy consumption. When leakage energy comes into play as well, the idle memory banks must be put in a low-leakage `dormant' state to save static energy when not accessed. The energy savings must be large enough to compensate the energy overhead spent by changing the bank status from active to dormant, then back to active again. This paper addresses the problem of energy-aware on-chip memory banking, taking into account - during the exploration of the search space - the idleness time intervals of the data mapped into the memory banks. As on-chip storage, we target scratch-pad memories (SPMs) since they are commonly used in embedded systems as an alternative to caches. The proposed approach proved to be computationally fast and very efficient when tested for several data-intensive applications, whose behavioral specifications contain Multidimensional arrays as main data structures.