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Team Show image information

Team

Linus Witschen

Contact
 Linus  Witschen

Computer Engineering

Wissenschaftlicher Mitarbeiter

Phone:
+49 5251 60-1729
Office:
O3.119
Web:
Visitor:
Pohlweg 51
33098 Paderborn

Approximate Computing

You can find the repository of our Approximate Circuit Synthesis framework here:
CIRCA: A Modular and Extensible Framework for Approximate Circuit Generation

Publications


Open list in Research Information System

Conferences

Jump Search: A Fast Technique for the Synthesis of Approximate Circuits

L.M. Witschen, H. Ghasemzadeh Mohammadi, M. Artmann, M. Platzner, in: Proceedings of the 2019 on Great Lakes Symposium on VLSI - GLSVLSI '19, ACM, 2019

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 (JS) method which seeks to reduce the runtime of an approximation process by reducing the number of expensive synthesis and verification steps. To reduce the runtime, JS computes impact factors for each approximation candidate in the circuit to create a selection of approximate circuits without invoking synthesis or verification processes. We denote the selection as path from which JS determines the final solution. In our experimental results, JS achieved speed-ups of up to 57x while area savings remain comparable to the reference search method, Simulated Annealing.

@inproceedings{Witschen_Ghasemzadeh Mohammadi_Artmann_Platzner_2019, place={New York, NY, USA}, title={Jump Search: A Fast Technique for the Synthesis of Approximate Circuits}, DOI={10.1145/3299874.3317998}, booktitle={Proceedings of the 2019 on Great Lakes Symposium on VLSI  - GLSVLSI ’19}, publisher={ACM}, author={Witschen, Linus Matthias and Ghasemzadeh Mohammadi, Hassan and Artmann, Matthias and Platzner, Marco}, year={2019} }


A Zynq-based dynamically reconfigurable high density myoelectric prosthesis controller

A. Boschmann, G. Thombansen, L.M. Witschen, A. Wiens, M. Platzner, in: Design, Automation and Test in Europe (DATE), 2017

@inproceedings{Boschmann_Thombansen_Witschen_Wiens_Platzner_2017, title={A Zynq-based dynamically reconfigurable high density myoelectric prosthesis controller}, DOI={10.23919/DATE.2017.7927137}, booktitle={Design, Automation and Test in Europe (DATE)}, author={Boschmann, Alexander and Thombansen, Georg and Witschen, Linus Matthias and Wiens, Alex and Platzner, Marco}, year={2017} }


FPGA-based acceleration of high density myoelectric signal processing

A. Boschmann, A. Agne, L.M. Witschen, G. Thombansen, F. Kraus, M. Platzner, in: 2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig), IEEE, 2016

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 off-the-shelf reconfigurable processing platform, we present a solution that is able to compute prosthesis control signals from multi-channel EMG input with up to 256 channels with a maximum processing delay of less than a single millisecond. While the presented system is able to perform training as well as classification, most of our efforts were focused on the acceleration of the feature extraction units, achieving a speed-up of 6.7 for feature extraction alone, and 4.8 for the total processing time as compared to a software only solution.

@inproceedings{Boschmann_Agne_Witschen_Thombansen_Kraus_Platzner_2016, title={FPGA-based acceleration of high density myoelectric signal processing}, DOI={10.1109/reconfig.2015.7393312}, booktitle={2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig)}, publisher={IEEE}, author={Boschmann, Alexander and Agne, Andreas and Witschen, Linus Matthias and Thombansen, Georg and Kraus, Florian and Platzner, Marco}, year={2016} }


Journal Articles

CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation

L.M. Witschen, T. Wiersema, H. Ghasemzadeh Mohammadi, M. Awais, M. Platzner, Microelectronics Reliability (2019), 99, pp. 277-290

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-based approximate circuit generation. CIRCA is developed with a focus on modularity and extensibility. We present the architecture of CIRCA with its clear separation into stages and functional blocks, report on the current prototype, and show initial experiments.

@article{Witschen_Wiersema_Ghasemzadeh Mohammadi_Awais_Platzner_2019, title={CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation}, volume={99}, DOI={10.1016/j.microrel.2019.04.003}, journal={Microelectronics Reliability}, publisher={Elsevier}, author={Witschen, Linus Matthias and Wiersema, Tobias and Ghasemzadeh Mohammadi, Hassan and Awais, Muhammad and Platzner, Marco}, year={2019}, pages={277–290} }


Zynq-based acceleration of robust high density myoelectric signal processing

A. Boschmann, A. Agne, G. Thombansen, L.M. Witschen, F. Kraus, M. Platzner, Journal of Parallel and Distributed Computing (2018), 123, pp. 77-89

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 accelerating two inherently different high density EMG-based control algorithms. The first hardware accelerated design achieves speed-ups of up to 4.8 over the software-only solution, allowing for a processing delay lower than the sample period of 1 ms. The second system achieved a speed-up of 5.5 over the software-only version and operates at a still satisfactory low processing delay of up to 15 ms while providing a higher reliability and robustness against electrode shift and noisy channels.

@article{Boschmann_Agne_Thombansen_Witschen_Kraus_Platzner_2018, title={Zynq-based acceleration of robust high density myoelectric signal processing}, volume={123}, DOI={10.1016/j.jpdc.2018.07.004}, journal={Journal of Parallel and Distributed Computing}, publisher={Elsevier}, author={Boschmann, Alexander and Agne, Andreas and Thombansen, Georg and Witschen, Linus Matthias and Kraus, Florian and Platzner, Marco}, year={2018}, pages={77–89} }


Master's Theses

A Framework for the Synthesis of Approximate Circuits

L.M. Witschen, Master's thesis, Universität Paderborn, 2017

@book{Witschen_2017, title={A Framework for the Synthesis of Approximate Circuits}, publisher={Universität Paderborn}, author={Witschen, Linus Matthias}, year={2017} }


Preprint

CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation

L.M. Witschen, T. Wiersema, H. Ghasemzadeh Mohammadi, M. Awais, M. Platzner, in: Third Workshop on Approximate Computing (AxC 2018), 2018

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-based approximate circuit generation. CIRCA is developed with a focus on modularity and extensibility. We present the architecture of CIRCA with its clear separation into stages and functional blocks, report on the current prototype, and show initial experiments.

@article{Witschen_Wiersema_Ghasemzadeh Mohammadi_Awais_Platzner, title={CIRCA: Towards a Modular and Extensible Framework for Approximate Circuit Generation}, journal={Third Workshop on Approximate Computing (AxC 2018)}, author={Witschen, Linus Matthias and Wiersema, Tobias and Ghasemzadeh Mohammadi, Hassan and Awais, Muhammad and Platzner, Marco} }


Making the Case for Proof-carrying Approximate Circuits

L.M. Witschen, T. Wiersema, M. Platzner, in: 4th Workshop On Approximate Computing (WAPCO 2018), 2018

@article{Witschen_Wiersema_Platzner_2018, title={Making the Case for Proof-carrying Approximate Circuits}, journal={4th Workshop On Approximate Computing (WAPCO 2018)}, author={Witschen, Linus Matthias and Wiersema, Tobias and Platzner, Marco}, year={2018} }


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