The Experts below are selected from a list of 24 Experts worldwide ranked by ideXlab platform
Walter Stechele - One of the best experts on this subject based on the ideXlab platform.
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NorCAS - Model-Based Design Space Exploration for Approximate Image Processing on FPGA
2020 IEEE Nordic Circuits and Systems Conference (NorCAS), 2020Co-Authors: Manu Manuel, Arne Kreddig, Simon Conrady, Nguyen Anh Vu Doan, Walter StecheleAbstract:Increasing demands on computational performance are outpacing the technological improvements in computer hardware. Approximate computing provides a recent approach to bridge this gap by exploiting the error resilience of applications and trading in quality for less resource usage. Research in this field has introduced numerous approximation methods. Combining multiple methods can further increase the benefits for complex systems. Because of error propagation and potential interactions between components, the approximation parameters cannot be optimized individually and therefore the design space grows exponentially across all approximations. Focusing on FPGA-based systems, we propose a methodology to explore this design space using a multi-objective genetic algorithm guided by appropriate models which estimate resource demands and anticipated quality degradation without time-consuming synthesis. The effectiveness of our approach is experimentally evaluated on a typical Image Color Processing pipeline considering multiple approximation methods. Our results show that the methodology is able to find a wide range of Pareto-optimal solutions, among which the desired quality-resource trade-off can be chosen.
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CANDAR (Workshops) - Parameter Optimization of Approximate Image Processing Algorithms in FPGAs
2020 Eighth International Symposium on Computing and Networking Workshops (CANDARW), 2020Co-Authors: Nguyen Anh Vu Doan, Manu Manuel, Arne Kreddig, Simon Conrady, Walter StecheleAbstract:Approximate computing has been proposed as a paradigm for contexts where resilience of applications to errors can be exploited, e.g. allowing to trade quality off for power/energy or hardware resources. Numerous approximation methodologies have been introduced in the literature and combining several of them can result in improved benefits. However, as approximation techniques require to be parametrized to control the loss of accuracy, using multiple ones implies to explore larger parameter sets. Furthermore, combined approximation methods can interact and influence the error propagation, adding to the optimization complexity. In this work, we propose an optimization model, targeted for a multi-objective genetic algorithm, to perform design space exploration simultaneously on all the parameters for each of the approximation techniques used in a system. We tailor the encoding and genetic operations for an Image Color Processing application so that the genetic algorithm can converge properly to a Pareto front with good diversity. The optimization is carried out for trade-offs between Image quality, FPGA hardware resource, and power. The results show that the proposed model can achieve the design space exploration and converge to a Pareto front that offers a wide range of trade-offs to choose from, while taking into account the potential interactions between the combined approximation techniques.
Liu Wei - One of the best experts on this subject based on the ideXlab platform.
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Image Color Processing and its Conversion of Computerrelated Equipment
Coal Technology, 2013Co-Authors: Liu WeiAbstract:Computer digital technology promote the development of computer-related equipment such as digital cameras,camcorders,computer monitors,printing equipment and other applications more widely.Natural picture is the main source of such Image information,the Image display and print is the main function of the Image-related device,the Image Processing technology is an indispensable means today a number of science and technology applications,Image Color Processing and conversion is the key technology.The paper first an overview of computer Image Processing technology,and introduces details of the Image Color Processing and conversion.
Nguyen Anh Vu Doan - One of the best experts on this subject based on the ideXlab platform.
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NorCAS - Model-Based Design Space Exploration for Approximate Image Processing on FPGA
2020 IEEE Nordic Circuits and Systems Conference (NorCAS), 2020Co-Authors: Manu Manuel, Arne Kreddig, Simon Conrady, Nguyen Anh Vu Doan, Walter StecheleAbstract:Increasing demands on computational performance are outpacing the technological improvements in computer hardware. Approximate computing provides a recent approach to bridge this gap by exploiting the error resilience of applications and trading in quality for less resource usage. Research in this field has introduced numerous approximation methods. Combining multiple methods can further increase the benefits for complex systems. Because of error propagation and potential interactions between components, the approximation parameters cannot be optimized individually and therefore the design space grows exponentially across all approximations. Focusing on FPGA-based systems, we propose a methodology to explore this design space using a multi-objective genetic algorithm guided by appropriate models which estimate resource demands and anticipated quality degradation without time-consuming synthesis. The effectiveness of our approach is experimentally evaluated on a typical Image Color Processing pipeline considering multiple approximation methods. Our results show that the methodology is able to find a wide range of Pareto-optimal solutions, among which the desired quality-resource trade-off can be chosen.
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CANDAR (Workshops) - Parameter Optimization of Approximate Image Processing Algorithms in FPGAs
2020 Eighth International Symposium on Computing and Networking Workshops (CANDARW), 2020Co-Authors: Nguyen Anh Vu Doan, Manu Manuel, Arne Kreddig, Simon Conrady, Walter StecheleAbstract:Approximate computing has been proposed as a paradigm for contexts where resilience of applications to errors can be exploited, e.g. allowing to trade quality off for power/energy or hardware resources. Numerous approximation methodologies have been introduced in the literature and combining several of them can result in improved benefits. However, as approximation techniques require to be parametrized to control the loss of accuracy, using multiple ones implies to explore larger parameter sets. Furthermore, combined approximation methods can interact and influence the error propagation, adding to the optimization complexity. In this work, we propose an optimization model, targeted for a multi-objective genetic algorithm, to perform design space exploration simultaneously on all the parameters for each of the approximation techniques used in a system. We tailor the encoding and genetic operations for an Image Color Processing application so that the genetic algorithm can converge properly to a Pareto front with good diversity. The optimization is carried out for trade-offs between Image quality, FPGA hardware resource, and power. The results show that the proposed model can achieve the design space exploration and converge to a Pareto front that offers a wide range of trade-offs to choose from, while taking into account the potential interactions between the combined approximation techniques.
Manu Manuel - One of the best experts on this subject based on the ideXlab platform.
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NorCAS - Model-Based Design Space Exploration for Approximate Image Processing on FPGA
2020 IEEE Nordic Circuits and Systems Conference (NorCAS), 2020Co-Authors: Manu Manuel, Arne Kreddig, Simon Conrady, Nguyen Anh Vu Doan, Walter StecheleAbstract:Increasing demands on computational performance are outpacing the technological improvements in computer hardware. Approximate computing provides a recent approach to bridge this gap by exploiting the error resilience of applications and trading in quality for less resource usage. Research in this field has introduced numerous approximation methods. Combining multiple methods can further increase the benefits for complex systems. Because of error propagation and potential interactions between components, the approximation parameters cannot be optimized individually and therefore the design space grows exponentially across all approximations. Focusing on FPGA-based systems, we propose a methodology to explore this design space using a multi-objective genetic algorithm guided by appropriate models which estimate resource demands and anticipated quality degradation without time-consuming synthesis. The effectiveness of our approach is experimentally evaluated on a typical Image Color Processing pipeline considering multiple approximation methods. Our results show that the methodology is able to find a wide range of Pareto-optimal solutions, among which the desired quality-resource trade-off can be chosen.
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CANDAR (Workshops) - Parameter Optimization of Approximate Image Processing Algorithms in FPGAs
2020 Eighth International Symposium on Computing and Networking Workshops (CANDARW), 2020Co-Authors: Nguyen Anh Vu Doan, Manu Manuel, Arne Kreddig, Simon Conrady, Walter StecheleAbstract:Approximate computing has been proposed as a paradigm for contexts where resilience of applications to errors can be exploited, e.g. allowing to trade quality off for power/energy or hardware resources. Numerous approximation methodologies have been introduced in the literature and combining several of them can result in improved benefits. However, as approximation techniques require to be parametrized to control the loss of accuracy, using multiple ones implies to explore larger parameter sets. Furthermore, combined approximation methods can interact and influence the error propagation, adding to the optimization complexity. In this work, we propose an optimization model, targeted for a multi-objective genetic algorithm, to perform design space exploration simultaneously on all the parameters for each of the approximation techniques used in a system. We tailor the encoding and genetic operations for an Image Color Processing application so that the genetic algorithm can converge properly to a Pareto front with good diversity. The optimization is carried out for trade-offs between Image quality, FPGA hardware resource, and power. The results show that the proposed model can achieve the design space exploration and converge to a Pareto front that offers a wide range of trade-offs to choose from, while taking into account the potential interactions between the combined approximation techniques.
Simon Conrady - One of the best experts on this subject based on the ideXlab platform.
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NorCAS - Model-Based Design Space Exploration for Approximate Image Processing on FPGA
2020 IEEE Nordic Circuits and Systems Conference (NorCAS), 2020Co-Authors: Manu Manuel, Arne Kreddig, Simon Conrady, Nguyen Anh Vu Doan, Walter StecheleAbstract:Increasing demands on computational performance are outpacing the technological improvements in computer hardware. Approximate computing provides a recent approach to bridge this gap by exploiting the error resilience of applications and trading in quality for less resource usage. Research in this field has introduced numerous approximation methods. Combining multiple methods can further increase the benefits for complex systems. Because of error propagation and potential interactions between components, the approximation parameters cannot be optimized individually and therefore the design space grows exponentially across all approximations. Focusing on FPGA-based systems, we propose a methodology to explore this design space using a multi-objective genetic algorithm guided by appropriate models which estimate resource demands and anticipated quality degradation without time-consuming synthesis. The effectiveness of our approach is experimentally evaluated on a typical Image Color Processing pipeline considering multiple approximation methods. Our results show that the methodology is able to find a wide range of Pareto-optimal solutions, among which the desired quality-resource trade-off can be chosen.
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CANDAR (Workshops) - Parameter Optimization of Approximate Image Processing Algorithms in FPGAs
2020 Eighth International Symposium on Computing and Networking Workshops (CANDARW), 2020Co-Authors: Nguyen Anh Vu Doan, Manu Manuel, Arne Kreddig, Simon Conrady, Walter StecheleAbstract:Approximate computing has been proposed as a paradigm for contexts where resilience of applications to errors can be exploited, e.g. allowing to trade quality off for power/energy or hardware resources. Numerous approximation methodologies have been introduced in the literature and combining several of them can result in improved benefits. However, as approximation techniques require to be parametrized to control the loss of accuracy, using multiple ones implies to explore larger parameter sets. Furthermore, combined approximation methods can interact and influence the error propagation, adding to the optimization complexity. In this work, we propose an optimization model, targeted for a multi-objective genetic algorithm, to perform design space exploration simultaneously on all the parameters for each of the approximation techniques used in a system. We tailor the encoding and genetic operations for an Image Color Processing application so that the genetic algorithm can converge properly to a Pareto front with good diversity. The optimization is carried out for trade-offs between Image quality, FPGA hardware resource, and power. The results show that the proposed model can achieve the design space exploration and converge to a Pareto front that offers a wide range of trade-offs to choose from, while taking into account the potential interactions between the combined approximation techniques.
