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[1]
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Axel Jantsch, Nikil Dutt, and Amir M. Rahmani.
Self-awareness in systems on chip -- a survey.
IEEE Design Test, 34(6):1--19, December 2017.
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[2]
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Anil Kanduri, Mohammad-Hashem Haghbayan, Amir M. Rahmani, Pasi Liljeberg, Axel
Jantsch, Hannu Tenhunen, and Nikil Dutt.
Accuracy aware power management for many-core systems running error
resilient applications.
IEEE Transactions on Very Large Scale Integration (VLSI)
Systems, 25(10), October 2017.
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DOI ]
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[3]
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Nikil Dutt, Amir M. Rahmani, and Axel Jantsch.
Empowering autonomy through self-awareness in MPSoCs.
In Proceedings of the IEEE NEWCAS Conference, Strasbourg,
France, June 2017.
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[4]
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Christian Krieg, Clifford Wolf, Axel Jantsch, and Tanja Zseby.
Toggle MUX: How X-optimism can lead to malicious hardware.
In Proceedins of the Design Automation Conference (DAC),
Austin, Texas, USA, June 2017.
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[5]
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Carna Radojicic, Christoph Grimm, Axel Jantsch, and Michael Rathmair.
Towards verification of uncertain cyber-physical systems.
In Erika Ábrahám and Sergiy Bogomolov, editors,
Proceedings 3rd International Workshop on Symbolic and Numerical Methods for
Reachability Analysis, Uppsala, Sweden, 22nd April 2017, volume 247 of
Electronic Proceedings in Theoretical Computer Science, pages 1--17. Open
Publishing Association, June 2017.
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[6]
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Junshi Wang, Masoumeh Ebrahimi, Letian Huang, Qiang Li, Guangjun Li, and Axel
Jantsch.
Minimizing the system impact of router faults by means of
reconfiguration and adaptive routing.
Microprocessors and Microsystems, 51:252 -- 263, June 2017.
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[7]
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M. Wess, P. D. S. Manoj, and A. Jantsch.
Neural network based ECG anomaly detection on FPGA and trade-off
analysis.
In 2017 IEEE International Symposium on Circuits and Systems
(ISCAS), pages 1--4, May 2017.
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[8]
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M. Götzinger, N. TaheriNejad, H. A. Kholerdi, and A. Jantsch.
On the design of context-aware health monitoring without a priori
knowledge; an AC-motor case-study.
In 2017 IEEE 30th Canadian Conference on Electrical and Computer
Engineering (CCECE), pages 1--5, April 2017.
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[9]
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M. H. Haghbayan, A. M. Rahmani, P. Liljeberg, A. Jantsch, A. Miele,
C. Bolchini, and H. Tenhunen.
Can dark silicon be exploited to prolong system lifetime?
IEEE Design Test, 34(2):51--59, April 2017.
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[10]
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Arman Anzanpour, Iman Azimi, Maximilian Götzinger, Amir M. Rahmani, Nima
TaheriNejad, Pasi Liljeberg, Axel Jantsch, and Nikil Dutt.
Self-awareness in remote health monitoring systems using wearable
electronics.
In Proceedings of Design and Test Europe Conference (DATE),
Lausanne, Switzerland, March 2017.
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[11]
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Maximilian Götzinger, Martin Pongratz, Amir M. Rahmani, and Axel Jantsch.
Parallelized flight path prediction using a graphics processing unit.
In Proceedings of the International Conference on Computer
Vision Theory and Applications (VISAPP), Portugal, February 2017.
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[12]
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A. M. Rahmani, M. H. Haghbayan, A. Miele, P. Liljeberg, A. Jantsch, and
H. Tenhunen.
Reliability-aware runtime power management for many-core systems in
the dark silicon era.
IEEE Transactions on Very Large Scale Integration (VLSI)
Systems, 25(2):427--440, February 2017.
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[13]
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A. Jantsch and N. Dutt.
Guest editorial: Special issue on self-aware systems on chip.
IEEE Design Test, 34(6):6--7, Dec 2017.
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[14]
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Ingo Sander, Axel Jantsch, and Seyed-Hosein Attarzadeh-Niaki.
Forsyde: System design using a functional language and models of
computation.
In Soonhoi Ha and Jürgen Teich, editors, Handbook of
Hardware/Software Codesign, pages 1--42. Springer Netherlands, Dordrecht,
2017.
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The ForSyDe methodology aims to push system design
to a higher level of abstraction by combining the
functional programming paradigm with the theory of
Models of Computation (MoCs). A key concept of
ForSyDe is the use of higher-order functions as
process constructors to create processes. This leads
to well-defined and well-structured ForSyDe models
and gives a solid base for formal analysis. The book
chapter introduces the basic concepts of the ForSyDe
modeling framework and presents libraries for
several MoCs and MoC interfaces for the modeling of
heterogeneous systems, including support for the
modeling of run-time reconfigurable processes. The
formal nature of ForSyDe enables transformational
design refinement using both semantic-preserving and
nonsemantic-preserving design transformations. The
chapter also introduces a general synthesis concept
based on process constructors, which is exemplified
by means of a hardware synthesis tool for
synchronous ForSyDe models. Most examples in the
chapter are modeled with the Haskell version of
ForSyDe. However, to illustrate that ForSyDe is
language-independent, the chapter also contains a
short overview of SystemC-ForSyDe.
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[15]
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Lydia C. Siafara, Hedyeh A. Kholerdi, Aleksey Bratukhin, Nima TaheriNejad,
Alexander Wendt, Axel Jantsch, Albert Treytl, and Thilo Sauter.
SAMBA: A self-aware health monitoring architecture for distributed
industrial systems.
In The 43rd Annual Conference of the IEEE Industrial Electronics
Society, 2017.
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