The Experts below are selected from a list of 40887 Experts worldwide ranked by ideXlab platform
Radu Marculescu - One of the best experts on this subject based on the ideXlab platform.
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Energy- And performance-aware mapping for regular NoC architectures
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2005Co-Authors: Jingcao Hu, Radu MarculescuAbstract:In this paper, we present an algorithm which automatically maps a given set of intellectual property onto a generic regular network-on-chip (NoC) architecture and constructs a deadlock-free deterministic routing function such that the total communication energy is minimized. At the same time, the performance of the resulting communication system is guaranteed to satisfy the specified design constraints through bandwidth reservation. As the main theoretical contribution, we first formulate the problem of energy- and performance-aware mapping in a topological sense, and show how the routing flexibility can be exploited to expand the solution space and improve the solution quality. An efficient branch-and-bound algorithm is then proposed to solve this problem. Experimental results show that the proposed algorithm is very fast, and significant communication energy savings can be achieved. For instance, for a complex video/Audio Application, 51.7% communication energy savings have been observed, on average, compared to an ad hoc implementation.
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communication aware task scheduling and voltage selection for total systems energy minimization
International Conference on Computer Aided Design, 2003Co-Authors: G V Varatka, Radu MarculescuAbstract:In this paper, we present an interprocessor communication-aware task scheduling algorithm applicable to a multiprocessor system executing an Application with dependent tasks. Our algorithm takes the Application task graph and the architecture graph as inputs, assigns the tasks to processors and then schedules them. As main theoretical contribution, the algorithm we propose reduces the overall systems energy by (i) reducing the total interprocessor communication and (ii) executing certain cycles at a lower voltage level. Experimental results show that by tuning the parameter for communication awareness, a schedule using our algorithm can reduce up to 80% interprocessor communication in a complex video/Audio Application (compared to a schedule which is only voltage-selection aware) without losing much in the number of cycles executed at lower voltage.
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exploiting the routing flexibility for energy performance aware mapping of regular noc architectures
Design Automation and Test in Europe, 2003Co-Authors: Jingcao Hu, Radu MarculescuAbstract:In this paper, we present an algorithm which automatically maps the IPs onto a generic regular Network on Chip (NoC) architecture and constructs a deadlock-free deterministic routing function such that the total communication energy is minimized. At the same time, the performance of the resulting communication system is guaranteed to satisfy the specified constraints through bandwidth reservation. As the main contribution, we first formulate the problem of energy/performance aware mapping, in a topological sense, and show how the routing flexibility can be exploited to expand the solution space and improve the solution quality. An efficient branch-and-bound algorithm is then described to solve this problem. Experimental results show that the proposed algorithm is very fast, and significant energy savings can be achieved. For instance, for a complex video/Audio Application, 51.7% energy savings have been observed, on average, compared to an ad-hoc implementation.
Jingcao Hu - One of the best experts on this subject based on the ideXlab platform.
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Energy- And performance-aware mapping for regular NoC architectures
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2005Co-Authors: Jingcao Hu, Radu MarculescuAbstract:In this paper, we present an algorithm which automatically maps a given set of intellectual property onto a generic regular network-on-chip (NoC) architecture and constructs a deadlock-free deterministic routing function such that the total communication energy is minimized. At the same time, the performance of the resulting communication system is guaranteed to satisfy the specified design constraints through bandwidth reservation. As the main theoretical contribution, we first formulate the problem of energy- and performance-aware mapping in a topological sense, and show how the routing flexibility can be exploited to expand the solution space and improve the solution quality. An efficient branch-and-bound algorithm is then proposed to solve this problem. Experimental results show that the proposed algorithm is very fast, and significant communication energy savings can be achieved. For instance, for a complex video/Audio Application, 51.7% communication energy savings have been observed, on average, compared to an ad hoc implementation.
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exploiting the routing flexibility for energy performance aware mapping of regular noc architectures
Design Automation and Test in Europe, 2003Co-Authors: Jingcao Hu, Radu MarculescuAbstract:In this paper, we present an algorithm which automatically maps the IPs onto a generic regular Network on Chip (NoC) architecture and constructs a deadlock-free deterministic routing function such that the total communication energy is minimized. At the same time, the performance of the resulting communication system is guaranteed to satisfy the specified constraints through bandwidth reservation. As the main contribution, we first formulate the problem of energy/performance aware mapping, in a topological sense, and show how the routing flexibility can be exploited to expand the solution space and improve the solution quality. An efficient branch-and-bound algorithm is then described to solve this problem. Experimental results show that the proposed algorithm is very fast, and significant energy savings can be achieved. For instance, for a complex video/Audio Application, 51.7% energy savings have been observed, on average, compared to an ad-hoc implementation.
Schmitz Thomas - One of the best experts on this subject based on the ideXlab platform.
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Modélisation non linéaire de la chaîne instrumentale pour guitare permettant son émulation en temps réel
Université de Liège Liège Belgique, 2019Co-Authors: Schmitz ThomasAbstract:Nonlinear systems identification and modeling is a central topic in many engineering areas since most real world devices may exhibit a nonlinear behavior. This thesis is devoted to the emulation of the nonlinear devices present in a guitar signal chain. The emulation aims to replace the hardware elements of the guitar signal chain in order to reduce its cost, its size, its weight and to increase its versatility. The challenge consists in enabling an accurate nonlinear emulation of the guitar signal chain while keeping the execution time of the model under the real time constraint. To do so, we have developed two methods. The first method developed in this thesis is based on a subclass of the Volterra series where only static nonlinearities are considered: the polynomial parallel cascade of Hammerstein models. The resulting method is called the Hammerstein Kernels Identification by Sine Sweep method (HKISS). According to the tests carried out in this thesis and to the results obtained, the method enables an accurate emulation of nonlinear Audio devices unless if the system to model is too far from an ideal Hammerstein one. The second method, based on neural networks, better generalizes to guitar signals and is well adapted to the emulation of guitar signal chain (e.g., tube and transistor amplifiers). We developed and compared eight models using different performance indexes including listening tests. The accuracy obtained depends on the tested Audio device and on the selected model but we have shown that the probability for a listener to be able to hear a difference between the target and the prediction could be less than 1%. This method could still be improved by training the neural networks with an objective function that better corresponds to the objective of this Audio Application, i.e., minimizing the audible difference between the target and the prediction. Finally, it is shown that these two methods enable an accurate emulation of a guitar signal chain while keeping a fast execution time which is required for real-time Audio Applications
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Modélisation non linéaire de la chaîne instrumentale pour guitare permettant son émulation en temps réel
Université de Liège Liège Belgique, 2019Co-Authors: Schmitz ThomasAbstract:audience: researcher, professional, studentNonlinear systems identification and modeling is a central topic in many engineering areas since most real world devices may exhibit a nonlinear behavior. This thesis is devoted to the emulation of the nonlinear devices present in a guitar signal chain. The emulation aims to replace the hardware elements of the guitar signal chain in order to reduce its cost, its size, its weight and to increase its versatility. The challenge consists in enabling an accurate nonlinear emulation of the guitar signal chain while keeping the execution time of the model under the real time constraint. To do so, we have developed two methods. The first method developed in this thesis is based on a subclass of the Volterra series where only static nonlinearities are considered: the polynomial parallel cascade of Hammerstein models. The resulting method is called the Hammerstein Kernels Identification by Sine Sweep method (HKISS). According to the tests carried out in this thesis and to the results obtained, the method enables an accurate emulation of nonlinear Audio devices unless if the system to model is too far from an ideal Hammerstein one. The second method, based on neural networks, better generalizes to guitar signals and is well adapted to the emulation of guitar signal chain (e.g., tube and transistor amplifiers). We developed and compared eight models using different performance indexes including listening tests. The accuracy obtained depends on the tested Audio device and on the selected model but we have shown that the probability for a listener to be able to hear a difference between the target and the prediction could be less than 1%. This method could still be improved by training the neural networks with an objective function that better corresponds to the objective of this Audio Application, i.e., minimizing the audible difference between the target and the prediction. Finally, it is shown that these two methods enable an accurate emulation of a guitar signal chain while keeping a fast execution time which is required for real-time Audio Applications
Quintana-ortí, Enrique S. - One of the best experts on this subject based on the ideXlab platform.
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Optimized Fundamental Signal Processing Operations For Energy Minimization on Heterogeneous Mobile Devices
IEEE, 2018Co-Authors: Belloch, Jose A., Badía José, Igual, Francisco D., González Alberto, Quintana-ortí, Enrique S.Abstract:Numerous signal processing Applications are emerging on both mobile and high-performance computing systems. These Applications are subject to responsiveness constraints for user interactivity and, at the same time, must be optimized for energy efficiency. The increasingly heterogeneous power-versus-performance profile of modern hardware introduces new opportunities for energy savings as well as challenges. In this line, recent Systems-On-Chip (SoC) composed of low-power multicore processors, combined with a small graphics accelerator (or GPU), yield a notable increment of the computational capacity while partially retaining the appealing low power consumption of embedded systems. This paper analyzes the potential of these new hardware systems to accelerate Applications that involve a large number of floating-point arithmetic operations mainly in the form of convolutions. To assess the performance, a headphone-based spatial Audio Application for mobile devices based on a Samsung Exynos 5422 SoC has been developed. We discuss different implementations and analyze the trade-offs between performance and energy efficiency for different scenarios and configurations. Our experimental results reveal that we can extend the battery lifetime of a device featuring such an architecture by a 238% by properly configuring and leveraging the computational resources
Joan Bruna - One of the best experts on this subject based on the ideXlab platform.
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Audio source separation with discriminative scattering networks
International Conference on Latent Variable Analysis and Signal Separation, 2015Co-Authors: Pablo Sprechmann, Joan Bruna, Yann LecunAbstract:Many monaural signal decomposition techniques proposed in the literature operate on a feature space consisting of a time-frequency representation of the input data. A challenge faced by these approaches is to effectively exploit the temporal dependencies of the signals at scales larger than the duration of a time-frame. In this work we propose to tackle this problem by modeling the signals using a time-frequency representation with multiple temporal resolutions. For this reason we use a signal representation that consists of a pyramid of wavelet scattering operators, which generalizes Constant Q Transforms CQT with extra layers of convolution and complex modulus. We first show that learning standard models with this multi-resolution setting improves source separation results over fixed-resolution methods. As study case, we use Non-Negative Matrix Factorizations NMF that has been widely considered in many Audio Application. Then, we investigate the inclusion of the proposed multi-resolution setting into a discriminative training regime. We discuss several alternatives using different deep neural network architectures, and our preliminary experiments suggest that in this task, finite impulse, multi-resolution Convolutional Networks are a competitive baseline compared to recurrent alternatives.
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Audio source separation with discriminative scattering networks
arXiv: Sound, 2014Co-Authors: Pablo Sprechmann, Joan BrunaAbstract:In this report we describe an ongoing line of research for solving single-channel source separation problems. Many monaural signal decomposition techniques proposed in the literature operate on a feature space consisting of a time-frequency representation of the input data. A challenge faced by these approaches is to effectively exploit the temporal dependencies of the signals at scales larger than the duration of a time-frame. In this work we propose to tackle this problem by modeling the signals using a time-frequency representation with multiple temporal resolutions. The proposed representation consists of a pyramid of wavelet scattering operators, which generalizes Constant Q Transforms (CQT) with extra layers of convolution and complex modulus. We first show that learning standard models with this multi-resolution setting improves source separation results over fixed-resolution methods. As study case, we use Non-Negative Matrix Factorizations (NMF) that has been widely considered in many Audio Application. Then, we investigate the inclusion of the proposed multi-resolution setting into a discriminative training regime. We discuss several alternatives using different deep neural network architectures.
