The Experts below are selected from a list of 342 Experts worldwide ranked by ideXlab platform
Theocharis Theocharides - One of the best experts on this subject based on the ideXlab platform.
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An Artificial Neural Network-Based Hotspot Prediction Mechanism for NoCs
2014Co-Authors: Elena Kakoulli, Vassos Soteriou, Theocharis TheocharidesAbstract:Abstract—Hotspots are network on-chip (NoC) routers or modules in systems on-chip (SoCs) which occasionally receive packetized traffic at a rate higher than they can consume it. This adverse phenomenon greatly reduces the performance of an NoC, especially in the case of today’s widely-employed Wormhole Flow-Control, as backpressure can cause the buffers of neighboring routers to quickly fill-up leading to a spatial spread in congestion that can cause the network to saturate. Even worse, such situations may lead to deadlocks. Thus, a hotspot prevention mechanism can be greatly beneficial, as it can potentially enable the interconnection system to adjust its behavior and prevent the rise of potential hotspots, subsequently sustaining NoC performance and efficiency. Unfortunately, hotspots cannot be known a-priori in NoCs used in general-purpose systems as application demands are not pre-determined unlike in application-specific SoCs, making hotspot prediction and subsequently prevention difficult. In this paper we present an artificial neural network-based hotspot prediction mechanism that can be potentially used in tandem with a hotspot avoidance mechanism for handling an unforeseen hotspot formation efficiently. The network uses buffer utilization statistical data to dynamically monitor the interconnect fabric, and reactively predicts the location of an about to-be-formed hotspot, allowing enough time for the system to react to these potential hotspots. The neural network is trained using synthetic traffic models, and evaluated using both synthetic and real application traces. Results indicate that a relatively small neural network can predict hotspot formation with accuracy ranges between 76 % to 92 % when evaluated on two different mesh NoCs. I
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Intelligent Hotspot Prediction for Network-on-Chip-Based Multicore Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2012Co-Authors: Elena Kakoulli, Vassos Soteriou, Theocharis TheocharidesAbstract:Hotspots are network-on-chip (NoC) routers or modules in multicore systems which occasionally receive packetized data from other networked element producers at a rate higher than they can consume it. This adverse phenomenon may greatly reduce the performance of NoCs, especially when Wormhole Flow-Control is employed, as backpressure can cause the buffers of neighboring routers to quickly fill-up leading to a spatial spread in congestion. This can cause the network to saturate prematurely where in the worst scenario the NoC may be rendered unrecoverable. Thus, a hotspot prevention mechanism can be greatly beneficial, as it can potentially enable the interconnection system to adjust its behavior and prevent the rise of potential hotspots, subsequently sustaining NoC performance. The inherent unevenness of traffic patterns in an NoC-based general-purpose multicore system such as a chip multiprocessor, due to the diverse and unpredictable access patterns of applications, produces unexpected hotspots whose appearance cannot be known a priori, as application demands are not predetermined, making hotspot prediction and subsequently prevention difficult. In this paper, we present an artificial neural network-based (ANN) hotspot prediction mechanism that can be potentially used in tandem with a hotspot avoidance or congestion-Control mechanism to handle unforeseen hotspot formations efficiently. The ANN uses online statistical data to dynamically monitor the interconnect fabric, and reactively predicts the location of an about to-be-formed hotspot(s), allowing enough time for the multicore system to react to these potential hotspots. Evaluation results indicate that a relatively lightweight ANN-based predictor can forecast hotspot formation(s) with an accuracy ranging from 65% to 92%.
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ISVLSI (Selected papers) - Intelligent NOC Hotspot Prediction
Lecture Notes in Electrical Engineering, 2011Co-Authors: Elena Kakoulli, Vassos Soteriou, Theocharis TheocharidesAbstract:Hotspots are Network on-Chip (NoC) routers or modules which occasionally receive packetized traffic at a higher rate that they can process. This phenomenon reduces the performance of an NoC, especially in the case Wormhole Flow-Control. Such situations may also lead to deadlocks, raising the need of a hotspot prevention mechanism. Such mechanism can potentially enable the system to adjust its behavior and prevent hotspot formation, subsequently sustaining performance and efficiency. This Chapter presents an Artificial Neural Network-based (ANN) hotspot prediction mechanism, potentially triggering a hotspot avoidance mechanism before the hotspot is formed. The ANN monitors buffer utilization and reactively predicts the location of an about to-be-formed hotspot, allowing enough time for the system to react to these potential hotspots. The neural network is trained using synthetic traffic models, and evaluated using both synthetic and real application traces. Results indicate that a relatively small neural network can predict hotspot formation with accuracy ranges between 76 and 92%.
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ISVLSI - An Artificial Neural Network-Based Hotspot Prediction Mechanism for NoCs
2010 IEEE Computer Society Annual Symposium on VLSI, 2010Co-Authors: Elena Kakoulli, Vassos Soteriou, Theocharis TheocharidesAbstract:Hotspots are network on-chip (NoC) routers or modules in systems on-chip (SoCs) which occasionally receive packetized traffic at a rate higher than they can consume it. This adverse phenomenon greatly reduces the performance of an NoC, especially in the case of today’s widely-employed Wormhole Flow-Control, as backpressure can cause the buffers of neighboring routers to quickly fill-up leading to a spatial spread in congestion that can cause the network to saturate. Even worse, such situations may lead to deadlocks. Thus, a hotspot prevention mechanism can be greatly beneficial, as it can potentially enable the interconnection system to adjust its behavior and prevent the rise of potential hotspots, subsequently sustaining NoC performance and efficiency. Unfortunately, hotspots cannot be known a-priori in NoCs used in general-purpose systems as application demands are not predetermined unlike in application-specific SoCs, making hotspot prediction and subsequently prevention difficult. In this paper we present an artificial neural network-based hotspot prediction mechanism that can be potentially used in tandem with a hotspot avoidance mechanism for handling an unforeseen hotspot formation efficiently. The network uses buffer utilization statistical data to dynamically monitor the interconnect fabric, and reactively predicts the location of an about to-be-formed hotspot, allowing enough time for the system to react to these potential hotspots. The neural network is trained using synthetic traffic models, and evaluated using both synthetic and real application traces. Results indicate that a relatively small neural network can predict hotspot formation with accuracy ranges between 76% to 92% when evaluated on two different mesh NoCs.
Elena Kakoulli - One of the best experts on this subject based on the ideXlab platform.
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An Artificial Neural Network-Based Hotspot Prediction Mechanism for NoCs
2014Co-Authors: Elena Kakoulli, Vassos Soteriou, Theocharis TheocharidesAbstract:Abstract—Hotspots are network on-chip (NoC) routers or modules in systems on-chip (SoCs) which occasionally receive packetized traffic at a rate higher than they can consume it. This adverse phenomenon greatly reduces the performance of an NoC, especially in the case of today’s widely-employed Wormhole Flow-Control, as backpressure can cause the buffers of neighboring routers to quickly fill-up leading to a spatial spread in congestion that can cause the network to saturate. Even worse, such situations may lead to deadlocks. Thus, a hotspot prevention mechanism can be greatly beneficial, as it can potentially enable the interconnection system to adjust its behavior and prevent the rise of potential hotspots, subsequently sustaining NoC performance and efficiency. Unfortunately, hotspots cannot be known a-priori in NoCs used in general-purpose systems as application demands are not pre-determined unlike in application-specific SoCs, making hotspot prediction and subsequently prevention difficult. In this paper we present an artificial neural network-based hotspot prediction mechanism that can be potentially used in tandem with a hotspot avoidance mechanism for handling an unforeseen hotspot formation efficiently. The network uses buffer utilization statistical data to dynamically monitor the interconnect fabric, and reactively predicts the location of an about to-be-formed hotspot, allowing enough time for the system to react to these potential hotspots. The neural network is trained using synthetic traffic models, and evaluated using both synthetic and real application traces. Results indicate that a relatively small neural network can predict hotspot formation with accuracy ranges between 76 % to 92 % when evaluated on two different mesh NoCs. I
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Intelligent Hotspot Prediction for Network-on-Chip-Based Multicore Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2012Co-Authors: Elena Kakoulli, Vassos Soteriou, Theocharis TheocharidesAbstract:Hotspots are network-on-chip (NoC) routers or modules in multicore systems which occasionally receive packetized data from other networked element producers at a rate higher than they can consume it. This adverse phenomenon may greatly reduce the performance of NoCs, especially when Wormhole Flow-Control is employed, as backpressure can cause the buffers of neighboring routers to quickly fill-up leading to a spatial spread in congestion. This can cause the network to saturate prematurely where in the worst scenario the NoC may be rendered unrecoverable. Thus, a hotspot prevention mechanism can be greatly beneficial, as it can potentially enable the interconnection system to adjust its behavior and prevent the rise of potential hotspots, subsequently sustaining NoC performance. The inherent unevenness of traffic patterns in an NoC-based general-purpose multicore system such as a chip multiprocessor, due to the diverse and unpredictable access patterns of applications, produces unexpected hotspots whose appearance cannot be known a priori, as application demands are not predetermined, making hotspot prediction and subsequently prevention difficult. In this paper, we present an artificial neural network-based (ANN) hotspot prediction mechanism that can be potentially used in tandem with a hotspot avoidance or congestion-Control mechanism to handle unforeseen hotspot formations efficiently. The ANN uses online statistical data to dynamically monitor the interconnect fabric, and reactively predicts the location of an about to-be-formed hotspot(s), allowing enough time for the multicore system to react to these potential hotspots. Evaluation results indicate that a relatively lightweight ANN-based predictor can forecast hotspot formation(s) with an accuracy ranging from 65% to 92%.
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ISVLSI (Selected papers) - Intelligent NOC Hotspot Prediction
Lecture Notes in Electrical Engineering, 2011Co-Authors: Elena Kakoulli, Vassos Soteriou, Theocharis TheocharidesAbstract:Hotspots are Network on-Chip (NoC) routers or modules which occasionally receive packetized traffic at a higher rate that they can process. This phenomenon reduces the performance of an NoC, especially in the case Wormhole Flow-Control. Such situations may also lead to deadlocks, raising the need of a hotspot prevention mechanism. Such mechanism can potentially enable the system to adjust its behavior and prevent hotspot formation, subsequently sustaining performance and efficiency. This Chapter presents an Artificial Neural Network-based (ANN) hotspot prediction mechanism, potentially triggering a hotspot avoidance mechanism before the hotspot is formed. The ANN monitors buffer utilization and reactively predicts the location of an about to-be-formed hotspot, allowing enough time for the system to react to these potential hotspots. The neural network is trained using synthetic traffic models, and evaluated using both synthetic and real application traces. Results indicate that a relatively small neural network can predict hotspot formation with accuracy ranges between 76 and 92%.
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ISVLSI - An Artificial Neural Network-Based Hotspot Prediction Mechanism for NoCs
2010 IEEE Computer Society Annual Symposium on VLSI, 2010Co-Authors: Elena Kakoulli, Vassos Soteriou, Theocharis TheocharidesAbstract:Hotspots are network on-chip (NoC) routers or modules in systems on-chip (SoCs) which occasionally receive packetized traffic at a rate higher than they can consume it. This adverse phenomenon greatly reduces the performance of an NoC, especially in the case of today’s widely-employed Wormhole Flow-Control, as backpressure can cause the buffers of neighboring routers to quickly fill-up leading to a spatial spread in congestion that can cause the network to saturate. Even worse, such situations may lead to deadlocks. Thus, a hotspot prevention mechanism can be greatly beneficial, as it can potentially enable the interconnection system to adjust its behavior and prevent the rise of potential hotspots, subsequently sustaining NoC performance and efficiency. Unfortunately, hotspots cannot be known a-priori in NoCs used in general-purpose systems as application demands are not predetermined unlike in application-specific SoCs, making hotspot prediction and subsequently prevention difficult. In this paper we present an artificial neural network-based hotspot prediction mechanism that can be potentially used in tandem with a hotspot avoidance mechanism for handling an unforeseen hotspot formation efficiently. The network uses buffer utilization statistical data to dynamically monitor the interconnect fabric, and reactively predicts the location of an about to-be-formed hotspot, allowing enough time for the system to react to these potential hotspots. The neural network is trained using synthetic traffic models, and evaluated using both synthetic and real application traces. Results indicate that a relatively small neural network can predict hotspot formation with accuracy ranges between 76% to 92% when evaluated on two different mesh NoCs.
Giovanni De Micheli - One of the best experts on this subject based on the ideXlab platform.
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a method to remove deadlocks in networks on chips with Wormhole Flow Control
Design Automation and Test in Europe, 2010Co-Authors: Ciprian Seiculescu, Srinivasan Murali, Luca Benini, Giovanni De MicheliAbstract:Networks-on-Chip (NoCs) are a promising interconnect paradigm to address the communication bottleneck of Systems-on-Chip (SoCs). Wormhole Flow Control is widely used as the transmission protocol in NoCs, as it offers high throughput and low latency. To match the application characteristics, customized irregular topologies and routing functions are used. With Wormhole Flow Control and custom irregular NoC topologies, deadlocks can occur during system operation. Ensuring a deadlock free operation of custom NoCs is a major challenge. In this paper, we address this important issue and present a method to remove deadlocks in application-specific NoCs. Our method can be applied to any NoC topology and routing function, and the potential deadlocks are removed by adding minimal number of virtual or physical channels. Experiments on a variety of realistic benchmarks show that our method results in a large reduction in the number of resources needed (88% on average) and NoC power consumption, area reduction (66% area savings on average) when compared to the state-of-the-art deadlock removal methods.
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DATE - A method to remove deadlocks in networks-on-chips with Wormhole Flow Control
2010 Design Automation & Test in Europe Conference & Exhibition (DATE 2010), 2010Co-Authors: Ciprian Seiculescu, Srinivasan Murali, Luca Benini, Giovanni De MicheliAbstract:Networks-on-Chip (NoCs) are a promising interconnect paradigm to address the communication bottleneck of Systems-on-Chip (SoCs). Wormhole Flow Control is widely used as the transmission protocol in NoCs, as it offers high throughput and low latency. To match the application characteristics, customized irregular topologies and routing functions are used. With Wormhole Flow Control and custom irregular NoC topologies, deadlocks can occur during system operation. Ensuring a deadlock free operation of custom NoCs is a major challenge. In this paper, we address this important issue and present a method to remove deadlocks in application-specific NoCs. Our method can be applied to any NoC topology and routing function, and the potential deadlocks are removed by adding minimal number of virtual or physical channels. Experiments on a variety of realistic benchmarks show that our method results in a large reduction in the number of resources needed (88% on average) and NoC power consumption, area reduction (66% area savings on average) when compared to the state-of-the-art deadlock removal methods.
Vassos Soteriou - One of the best experts on this subject based on the ideXlab platform.
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An Artificial Neural Network-Based Hotspot Prediction Mechanism for NoCs
2014Co-Authors: Elena Kakoulli, Vassos Soteriou, Theocharis TheocharidesAbstract:Abstract—Hotspots are network on-chip (NoC) routers or modules in systems on-chip (SoCs) which occasionally receive packetized traffic at a rate higher than they can consume it. This adverse phenomenon greatly reduces the performance of an NoC, especially in the case of today’s widely-employed Wormhole Flow-Control, as backpressure can cause the buffers of neighboring routers to quickly fill-up leading to a spatial spread in congestion that can cause the network to saturate. Even worse, such situations may lead to deadlocks. Thus, a hotspot prevention mechanism can be greatly beneficial, as it can potentially enable the interconnection system to adjust its behavior and prevent the rise of potential hotspots, subsequently sustaining NoC performance and efficiency. Unfortunately, hotspots cannot be known a-priori in NoCs used in general-purpose systems as application demands are not pre-determined unlike in application-specific SoCs, making hotspot prediction and subsequently prevention difficult. In this paper we present an artificial neural network-based hotspot prediction mechanism that can be potentially used in tandem with a hotspot avoidance mechanism for handling an unforeseen hotspot formation efficiently. The network uses buffer utilization statistical data to dynamically monitor the interconnect fabric, and reactively predicts the location of an about to-be-formed hotspot, allowing enough time for the system to react to these potential hotspots. The neural network is trained using synthetic traffic models, and evaluated using both synthetic and real application traces. Results indicate that a relatively small neural network can predict hotspot formation with accuracy ranges between 76 % to 92 % when evaluated on two different mesh NoCs. I
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Intelligent Hotspot Prediction for Network-on-Chip-Based Multicore Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2012Co-Authors: Elena Kakoulli, Vassos Soteriou, Theocharis TheocharidesAbstract:Hotspots are network-on-chip (NoC) routers or modules in multicore systems which occasionally receive packetized data from other networked element producers at a rate higher than they can consume it. This adverse phenomenon may greatly reduce the performance of NoCs, especially when Wormhole Flow-Control is employed, as backpressure can cause the buffers of neighboring routers to quickly fill-up leading to a spatial spread in congestion. This can cause the network to saturate prematurely where in the worst scenario the NoC may be rendered unrecoverable. Thus, a hotspot prevention mechanism can be greatly beneficial, as it can potentially enable the interconnection system to adjust its behavior and prevent the rise of potential hotspots, subsequently sustaining NoC performance. The inherent unevenness of traffic patterns in an NoC-based general-purpose multicore system such as a chip multiprocessor, due to the diverse and unpredictable access patterns of applications, produces unexpected hotspots whose appearance cannot be known a priori, as application demands are not predetermined, making hotspot prediction and subsequently prevention difficult. In this paper, we present an artificial neural network-based (ANN) hotspot prediction mechanism that can be potentially used in tandem with a hotspot avoidance or congestion-Control mechanism to handle unforeseen hotspot formations efficiently. The ANN uses online statistical data to dynamically monitor the interconnect fabric, and reactively predicts the location of an about to-be-formed hotspot(s), allowing enough time for the multicore system to react to these potential hotspots. Evaluation results indicate that a relatively lightweight ANN-based predictor can forecast hotspot formation(s) with an accuracy ranging from 65% to 92%.
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ISVLSI (Selected papers) - Intelligent NOC Hotspot Prediction
Lecture Notes in Electrical Engineering, 2011Co-Authors: Elena Kakoulli, Vassos Soteriou, Theocharis TheocharidesAbstract:Hotspots are Network on-Chip (NoC) routers or modules which occasionally receive packetized traffic at a higher rate that they can process. This phenomenon reduces the performance of an NoC, especially in the case Wormhole Flow-Control. Such situations may also lead to deadlocks, raising the need of a hotspot prevention mechanism. Such mechanism can potentially enable the system to adjust its behavior and prevent hotspot formation, subsequently sustaining performance and efficiency. This Chapter presents an Artificial Neural Network-based (ANN) hotspot prediction mechanism, potentially triggering a hotspot avoidance mechanism before the hotspot is formed. The ANN monitors buffer utilization and reactively predicts the location of an about to-be-formed hotspot, allowing enough time for the system to react to these potential hotspots. The neural network is trained using synthetic traffic models, and evaluated using both synthetic and real application traces. Results indicate that a relatively small neural network can predict hotspot formation with accuracy ranges between 76 and 92%.
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ISVLSI - An Artificial Neural Network-Based Hotspot Prediction Mechanism for NoCs
2010 IEEE Computer Society Annual Symposium on VLSI, 2010Co-Authors: Elena Kakoulli, Vassos Soteriou, Theocharis TheocharidesAbstract:Hotspots are network on-chip (NoC) routers or modules in systems on-chip (SoCs) which occasionally receive packetized traffic at a rate higher than they can consume it. This adverse phenomenon greatly reduces the performance of an NoC, especially in the case of today’s widely-employed Wormhole Flow-Control, as backpressure can cause the buffers of neighboring routers to quickly fill-up leading to a spatial spread in congestion that can cause the network to saturate. Even worse, such situations may lead to deadlocks. Thus, a hotspot prevention mechanism can be greatly beneficial, as it can potentially enable the interconnection system to adjust its behavior and prevent the rise of potential hotspots, subsequently sustaining NoC performance and efficiency. Unfortunately, hotspots cannot be known a-priori in NoCs used in general-purpose systems as application demands are not predetermined unlike in application-specific SoCs, making hotspot prediction and subsequently prevention difficult. In this paper we present an artificial neural network-based hotspot prediction mechanism that can be potentially used in tandem with a hotspot avoidance mechanism for handling an unforeseen hotspot formation efficiently. The network uses buffer utilization statistical data to dynamically monitor the interconnect fabric, and reactively predicts the location of an about to-be-formed hotspot, allowing enough time for the system to react to these potential hotspots. The neural network is trained using synthetic traffic models, and evaluated using both synthetic and real application traces. Results indicate that a relatively small neural network can predict hotspot formation with accuracy ranges between 76% to 92% when evaluated on two different mesh NoCs.
Ciprian Seiculescu - One of the best experts on this subject based on the ideXlab platform.
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a method to remove deadlocks in networks on chips with Wormhole Flow Control
Design Automation and Test in Europe, 2010Co-Authors: Ciprian Seiculescu, Srinivasan Murali, Luca Benini, Giovanni De MicheliAbstract:Networks-on-Chip (NoCs) are a promising interconnect paradigm to address the communication bottleneck of Systems-on-Chip (SoCs). Wormhole Flow Control is widely used as the transmission protocol in NoCs, as it offers high throughput and low latency. To match the application characteristics, customized irregular topologies and routing functions are used. With Wormhole Flow Control and custom irregular NoC topologies, deadlocks can occur during system operation. Ensuring a deadlock free operation of custom NoCs is a major challenge. In this paper, we address this important issue and present a method to remove deadlocks in application-specific NoCs. Our method can be applied to any NoC topology and routing function, and the potential deadlocks are removed by adding minimal number of virtual or physical channels. Experiments on a variety of realistic benchmarks show that our method results in a large reduction in the number of resources needed (88% on average) and NoC power consumption, area reduction (66% area savings on average) when compared to the state-of-the-art deadlock removal methods.
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DATE - A method to remove deadlocks in networks-on-chips with Wormhole Flow Control
2010 Design Automation & Test in Europe Conference & Exhibition (DATE 2010), 2010Co-Authors: Ciprian Seiculescu, Srinivasan Murali, Luca Benini, Giovanni De MicheliAbstract:Networks-on-Chip (NoCs) are a promising interconnect paradigm to address the communication bottleneck of Systems-on-Chip (SoCs). Wormhole Flow Control is widely used as the transmission protocol in NoCs, as it offers high throughput and low latency. To match the application characteristics, customized irregular topologies and routing functions are used. With Wormhole Flow Control and custom irregular NoC topologies, deadlocks can occur during system operation. Ensuring a deadlock free operation of custom NoCs is a major challenge. In this paper, we address this important issue and present a method to remove deadlocks in application-specific NoCs. Our method can be applied to any NoC topology and routing function, and the potential deadlocks are removed by adding minimal number of virtual or physical channels. Experiments on a variety of realistic benchmarks show that our method results in a large reduction in the number of resources needed (88% on average) and NoC power consumption, area reduction (66% area savings on average) when compared to the state-of-the-art deadlock removal methods.
