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Linus Witschen
University of Paderborn
7Publications
1H-index
9Citations
Publications 7
Newest
#1Linus Witschen (University of Paderborn)H-Index: 1
#2Muhammad Awais (University of Paderborn)H-Index: 1
Last.Marco Platzner (University of Paderborn)H-Index: 25
view all 5 authors...
Abstract Existing approaches and tools for the generation of approximate circuits often lack generality and are restricted to certain circuit types, approximation techniques, and quality assurance methods. Moreover, only few tools are publicly available. This hinders the development and evaluation of new techniques for approximating circuits and their comparison to previous approaches. In this paper, we first analyze and classify related approaches and then present CIRCA, our flexible framework ...
#1Linus Witschen (University of Paderborn)H-Index: 1
#2Hassan Ghasemzadeh Mohammadi (University of Paderborn)H-Index: 3
Last.Marco Platzner (University of Paderborn)H-Index: 25
view all 4 authors...
State-of-the-art frameworks for generating approximate circuits automatically explore the search space in an iterative process - often greedily. Synthesis and verification processes are invoked in each iteration to evaluate the found solutions and to guide the search algorithm. As a result, a large number of approximate circuits is subjected to analysis - leading to long runtimes - but only a few approximate circuits might form an acceptable solution. In this paper, we present our Jump Search (J...
#1Alexander Boschmann (University of Paderborn)H-Index: 6
#2Andreas Agne (University of Paderborn)H-Index: 8
Last.Marco Platzner (University of Paderborn)H-Index: 25
view all 6 authors...
Abstract Advances in electromyographic (EMG) sensor technology and machine learning algorithms have led to an increased research effort into high density EMG-based pattern recognition methods for prosthesis control. With the goal set on an autonomous multi-movement prosthesis capable of performing training and classification of an amputee’s EMG signals, the focus of this paper lies in the acceleration of the embedded signal processing chain. We present two Xilinx Zynq-based architectures for acc...
#1Linus WitschenH-Index: 1
#2Tobias WiersemaH-Index: 4
Last.Marco PlatznerH-Index: 25
view all 5 authors...
Existing approaches and tools for the generation of approximate circuits often lack generality and are restricted to certain circuit types, approximation techniques, and quality assurance methods. Moreover, only few tools are publicly available. This hinders the development and evaluation of new techniques for approximating circuits and their comparison to previous approaches. In this paper, we first analyze and classify related approaches and then present CIRCA, our flexible framework for search-...
Mar 1, 2017 in DATE (Design, Automation, and Test in Europe)
#1Alexander Boschmann (University of Paderborn)H-Index: 6
#2Georg Thombansen (University of Paderborn)H-Index: 1
Last.Marco Platzner (University of Paderborn)H-Index: 25
view all 5 authors...
The combination of high-density electromyographic (HD EMG) sensor technology and modern machine learning algorithms allows for intuitive and robust prosthesis control of multiple degrees of freedom. However, HD EMG real-time processing poses a challenge for common microprocessors in an embedded system. With the goal set on an autonomous prosthesis capable of performing training and classification of an amputee's HD EMG signals, the focus of this paper lies in the acceleration of the computationa...
Dec 1, 2015 in ReConFig (Reconfigurable Computing and FPGAs)
#1Alexander Boschmann (University of Paderborn)H-Index: 6
#2Andreas Agne (University of Paderborn)H-Index: 8
Last.Marco Platzner (University of Paderborn)H-Index: 25
view all 6 authors...
In recent years, advances in electromyographic (EMG) sensor technology and machine learning algorithms have led to an increased research effort into high density EMG based pattern recognition methods for prosthesis control. With the goal set on an autonomous multi-movement prosthesis that is capable of performing training and classification of an amputee’s EMG signals, the focus of this paper lies in the acceleration of the embedded signal processing chain. Using the Xilinx Zynq as a low-cost of...
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