publ-ML.bib
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@comment{{This file has been generated by bib2bib 1.99}}
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and ( not ( $key : "presentation" ))
and ( not ( $type : "techreport" ))
and ( not ( $type : "misc" ))
and ( not ( annotate : "not reviewed" )))
or $key = "hauer:2021a"
' /home/jantsch/text/papers/lit.bib}}
@comment{{Example entry for online references:
miscWinNT,
title = MS Windows NT Kernel Description,
howpublished = \urlhttp://web.archive.org/web/20080207010024/http://www.808multimedia.com/winnt/kernel.htm,
note = Accessed: 2010-09-30
}}
@comment{{Example:
miscWikiQuineMcCluskey,
title = Quine-McCluskey Algorithm,
author = Wikipedia ,
year = 2021,
howpublished =
\urlhttps://en.wikipedia.org/wiki/Quine%E2%80%93McCluskey_algorithm,
note = Accessed: 2021-08-11
}}
@inproceedings{bittner:2023b,
author = {Matthias Bittner and Sanaa Hobeichi and Muhammad
Zawish and Samo Diatta and Remigious Ozioko and
Sharon Xu and Axel Jantsch },
title = {An {LSTM}-based Downscaling Framework for {Australian}
Precipitation Projections },
key = {eml,cdl},
booktitle = {NeurIPS 2023 Workshop: Tackling Climate Change with
Machine Learning at the Conference on Neural
Information Processing Systems },
year = 2023,
address = {December},
url = {http://jantsch.se/AxelJantsch/papers/2023/MatthiasBittner-CCAI-NeurIPS.pdf}
}
@inproceedings{bittner:2023a,
author = {Matthias Bittner and Daniel Hauer and Christian
Stippel and Katharina Scheucher and Robin Sudhoff
and Axel Jantsch },
title = {Forecasting Critical Overloads based on
Heterogeneous Smart Grid Simulation },
key = {eml,cdl},
booktitle = { Proceedings of the International Conference on
Machine Learning and Applications },
year = 2023,
month = {December},
address = {Jacksonville, Florida, USA},
organization = {IEEE and AMLA},
url = {http://jantsch.se/AxelJantsch/papers/2023/MatthiasBittner-ICMLA.pdf}
}
@inproceedings{schnoell:2023a,
author = {Daniel Schn\"{o}ll and Matthias Wess and Matthias Bittner and Maximilian G\"{o}tzinger and Axel Jantsch },
title = {Fast, Quantization Aware {DNN} Training for Efficient {HW} Implementation},
key = {cdl,eml},
booktitle = { Proceedings of the 26th Euromicro Conference on Digital System Design (DSD)},
year = 2023,
month = {September},
address = {Durres, Albania},
url = {http://jantsch.se/AxelJantsch/papers/2023/DanielSchnoell-DSD.pdf}
}
@inproceedings{breuss:2023a,
author = {David Breuss and Maximilian G\"{o}tzinger and Jenny Vuong and Clemens Reisner and Axel Jantsch },
title = { {VADAR:} A Vision-based Anomaly Detection Algorithm for Railroads},
key = {cdl,eml},
booktitle = {Proceedings of the 26th Euromicro Conference on
Digital System Design (DSD)},
year = 2023,
month = {September},
address = {Durres, Albania},
url = {http://jantsch.se/AxelJantsch/papers/2023/DavidBreuss-DSD.pdf}
}
@inproceedings{wess:2023a,
author = {Matthias Wess and Dominik Dallinger and Daniel
Schn\"{o}ll and Matthias Bittner and Maximilian
G\"{o}tzinger and Axel Jantsch },
title = {Energy Profiling of {DNN} Accelerators},
key = {cdl,eml},
booktitle = {Proceedings of the 26th Euromicro Conference on
Digital System Design (DSD)},
year = 2023,
month = {September},
address = {Durres, Albania},
url = {http://jantsch.se/AxelJantsch/papers/2023/MatthiasWess-DSD.pdf}
}
@inproceedings{saqib:2023a,
author = { Eiraj Saqib and Isaac S\'{a}nchez Leal and Irida
Shallari and Axel Jantsch and Silvia Krug and
Mattias O'Nils },
title = { Optimizing the {IoT} Performance: A Case Study on
Pruning a Distributed {CNN} },
booktitle = {Proceedings of the IEEE Sensors Applications Symposium (SAS) },
year = 2023,
key = {eml},
url = {http://jantsch.se/AxelJantsch/papers/2023/EirajSaqib-SAS.pdf}
}
@inproceedings{leal:2023a,
author = { Isaac S\'{a}nchez Leal and Eiraj Saqib and Irida Shallari and
Axel Jantsch and Silvia Krug and Mattias O'Nils },
title = {Waist Tightening of {CNNs}: A Case study on Tiny YOLOv3 for Distributed IoT Implementations},
key = {eml},
booktitle = {Proceedings of the Real-time And intelliGent Edge
computing workshop (RAGE) },
year = 2023,
month = {May},
address = {San Antonio, Texas},
url = {http://jantsch.se/AxelJantsch/papers/2023/IsaacSanchezLeal-RAGE.pdf
}
}
@inproceedings{kotrba:2023a,
author = {Thomas Kotrba and Martin Lechner and Omair Sarwar
and Axel Jantsch },
title = {Multispectral Feature Fusion for Deep Object
Detection on Embedded NVIDIA Platforms },
key = {cdl,eml},
booktitle = { Design, Automation {\&} Test in Europe
Conference {\&} Exhibition ({DATE}) },
year = 2023,
month = {April},
address = {Antwerp, Belgium},
url = {http://jantsch.se/AxelJantsch/papers/2023/ThomasKotrba-DATE.pdf}
}
@article{lundstroem:2022a,
author = {Lundstr\"om, Adam and O'Nils, Mattias and Qureshi,
Faisal and Jantsch, Axel},
journal = {IEEE Access},
title = {Improving deep learning based anomaly detection on
multivariate time series through separated anomaly
scoring},
year = 2022,
pages = {1-1},
key = { eml },
doi = {10.1109/ACCESS.2022.3213038}
}
@article{mozelli:2021a,
author = {Amid Mozelli and Nima Taherinejad and Axel Jantsch},
title = {A Study on Confidence: an Unsupervised Multi-Agent
Machine Learning Experiment },
journal = {IEEE Design \& Test of Computers },
year = 2021,
key = {eml,cdl,selfaware},
issn = { 2168-2356 },
doi = { 10.1109/MDAT.2021.3078341 }
}
@article{leal:2021a,
author = {Isaac S\'{a}nchez Leal and Irida Shallari and Silvia
Krug and Axel Jantsch and Mattias O'Nils },
title = { Impact of Input Data on Intelligence Partitioning
Decisions for {IoT} Smart Camera Nodes },
journal = { Electronics },
year = 2021,
volume = 10,
number = 16,
key = {eml},
annote = { SCIE indexed },
issn = { 2079-9292 },
doi = {10.3390/electronics10161898},
url = {http://jantsch.se/AxelJantsch/papers/2021/IsaacLeal-MDPIElectronics.pdf}
}
@article{shallari:2021a,
author = {Shallari, Irida and S\'{a}nchez Leal, Isaac and Krug,
Silvia and Jantsch, Axel and O'Nils, Mattias},
journal = {IEEE Access},
title = {{Design space exploration on IoT node: Trade-offs in
processing and communication}},
year = 2021,
key = {eml},
doi = {10.1109/ACCESS.2021.3074875},
url = {http://jantsch.se/AxelJantsch/papers/2021/IridaShallari-IEEEAccess.pdf},
issn = {2169-3536}
}
@article{lechner:2024a,
author = {Martin Lechner and Axel Jantsch},
title = {TITLE TO BE DEFINED},
journal = {Under submission},
key = {eml,cdl},
year = 2024
}
@inproceedings{lechner:2022a,
author = {Martin Lechner and Axel Jantsch and Lukas Steindl},
title = {Study of {DNN}-based Ragweed Detection from Drones},
key = {eml,cdl},
booktitle = {Proceedings of International Conference on Embedded
Computer Systems: Architectures, Modeling and
Simulation (SAMOS)},
year = 2022,
month = {July},
address = {Samos, Greece},
url = {http://jantsch.se/AxelJantsch/papers/2022/MartinLechner-SAMOS.pdf}
}
@inproceedings{haas:2021a,
author = { Bernhard Haas and Alexander Wendt and Axel Jantsch
and Matthias Wess },
title = {Neural Network Compression Through Shunt Connections
and Knowledge Distillation for Semantic Segmentation
Problems },
key = {eml},
booktitle = {17th International Conference on Artificial
Intelligence Applications and Innovations (AIAI)},
month = {June},
doi = {https://doi.org/10.1007/978-3-030-79150-6},
year = 2021,
url = {https://link.springer.com/chapter/10.1007/978-3-030-79150-6_28}
}
@article{lechner:2021a,
author = {Martin Lechner and Axel Jantsch},
title = {Blackthorn: Latency Estimation Framework for {CNNs}
on Embedded {Nvidia} Platforms},
journal = {IEEE Access},
year = 2021,
key = {eml,cdl},
doi = {10.1109/ACCESS.2021.3101936},
url = {http://jantsch.se/AxelJantsch/papers/2021/MartinLechner-IEEEAccess.pdf}
}
@article{wess:2024a,
author = {Matthias Wess and Axel Jantsch},
title = {Confidence for Latency Estimation of {DNN}
Accelerators: A Blackbox Approach},
journal = {Under submission},
key = {eml,cdl},
year = 2024
}
@article{wess:2021a,
author = {Matthias Wess and Marco Ivanov and Christian Unger and
Anvesh Nookala and Alexander Wendt and Axel Jantsch},
journal = {IEEE Access},
title = {{ANNETTE}: Accurate Neural Network Execution Time
Estimation With Stacked Models},
year = 2021,
volume = 9,
pages = {3545-3556},
key = {eml,cdl},
abstract = {With new accelerator hardware for Deep Neural
Networks (DNNs), the computing power for Artificial
Intelligence (AI) applications has increased
rapidly. However, as DNN algorithms become more
complex and optimized for specific applications,
latency requirements remain challenging, and it is
critical to find the optimal points in the design
space. To decouple the architectural search from the
target hardware, we propose a time estimation
framework that allows for modeling the inference
latency of DNNs on hardware accelerators based on
mapping and layer-wise estimation models. The
proposed methodology extracts a set of models from
micro-kernel and multi-layer benchmarks and
generates a stacked model for mapping and network
execution time estimation. We compare estimation
accuracy and fidelity of the generated mixed models,
statistical models with the roofline model, and a
refined roofline model for evaluation. We test the
mixed models on the ZCU102 SoC board with Xilinx
Deep Neural Network Development Kit (DNNDK) and
Intel Neural Compute Stick 2 (NCS2) on a set of 12
state-of-the-art neural networks. It shows an
average estimation error of 3.47\% for the DNNDK and
7.44\% for the NCS2, outperforming the statistical
and analytical layer models for almost all selected
networks. For a randomly selected subset of 34
networks of the NASBench dataset, the mixed model
reaches fidelity of 0.988 in Spearman’s $\rho $ rank
correlation coefficient metric.},
doi = {10.1109/ACCESS.2020.3047259},
url = {http://jantsch.se/AxelJantsch/papers/2021/MatthiasWess-IEEEAccess.pdf},
issn = {2169-3536}
}
@inproceedings{colucci:2021a,
author = {Alessio Colucci and D\'avid Juh\'asz and Martin Mosbeck
and Alberto Marchisio and Semeen Rehman and Manfred
Kreutzer and G\"{u}nter Nadbath and Axel Jantsch and
Muhammad Shafique },
title = { {MLComp}: A Methodology for Machine Learning-based
Performance Estimation and Adaptive Selection of
{Pareto}-Optimal Compiler Optimization Sequences },
key = {eml},
booktitle = {Proceedings of the Design, Automation and Test in Europe Conference and Exhibition },
year = 2021,
month = {March},
url = {http://jantsch.se/AxelJantsch/papers/2021/DavidJuhasz-DATE.pdf}
}
@article{taherinejad:2020a,
author = {N. {TaheriNejad} and A. {Herkersdorf} and
A. {Jantsch}},
journal = {IEEE Design Test},
title = {Autonomous Systems, Trust and Guarantees},
year = 2020,
issn = {2168-2356},
doi = { 10.1109/MDAT.2020.3024145},
url = {http://jantsch.se/AxelJantsch/papers/2020/NimaTaherinejad-DesignAndTest.pdf},
key = {selfaware,eml,cdl},
pages = {1-1}
}
@incollection{wendt:2023a,
author = { Alexander Wendt and Horst Possegger and Matthias
Bittner and Daniel Schn\"{o}ll and Matthias Wess and
Du\v{s}an Mali\'{c} and Horst Bischof and Axel
Jantsch },
title = { A Pedestrian Detection Case Study for a Traffic
Light Controller },
booktitle = { Embedded Machine Learning for Cyber-Physical, IoT,
and Edge Computing - Software Optimizations and
Hardware/Software Codesign },
key = {eml,cdl},
publisher = {Springer},
year = 2023,
editor = {Sudeep Pasricha and Muhammad Shafique},
pages = {75--96},
doi = {10.1007/978-3-031-39932-9},
url = {https://link.springer.com/chapter/10.1007/978-3-031-39932-9_4}
}
@article{hoffmann:2020a,
author = {Henrik {Hoffmann} and Axel {Jantsch} and Nikil
D. {Dutt}},
journal = {Proceedings of the IEEE},
title = {Embodied Self-Aware Computing Systems},
year = 2020,
pages = {1-20},
key = {selfaware,eml,cdl},
doi = {10.1109/JPROC.2020.2977054},
issn = {1558-2256},
url = {http://jantsch.se/AxelJantsch/papers/2020/HankHoffmann-IEEEProceedings.pdf}
}
@inproceedings{lechner:2019a,
author = {Martin {Lechner} and Axel {Jantsch} and Sai
M. P. {Dinakarrao}},
booktitle = {2019 Tenth International Green and Sustainable
Computing Conference (IGSC)},
title = {{ResCoNN}: Resource-Efficient {FPGA}-Accelerated
{CNN} for Traffic Sign Classification},
year = 2019,
pages = {1-6},
key = {eml,cdl},
doi = {10.1109/IGSC48788.2019.8957186},
issn = {null},
month = {Oct}
}
@inproceedings{wendt:2020a,
author = { David Bechtold and Alexander Wendt and Axel Jantsch },
title = {Evaluation of Reinforcement Learning Methods for a Self-learning System},
booktitle = {Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020)},
year = 2020,
volume = 2,
month = {February},
address = {Valletta, Malta},
key = {selfaware,ml},
url = {http://jantsch.se/AxelJantsch/papers/2020/AlexWendt-SelfLearningAgent-ICAART.pdf
}
}
@article{bellman:2020a,
author = {Kerstin Bellman and Nikil Dutt and Lukas Esterle and
Andreas Herkersdorf and Axel Jantsch and C. Landauer
and P. R. Lewis and M. Platzner and N. TaheriNejad
and K. Tammem\"{a}e},
journal = {ACM Transactions on Cyber-Physical Systems},
title = {Self-aware Cyber-Physical Systems},
year = 2020,
key = {selfaware,eml,cdl},
pages = {1-24},
address = {New York, NY, USA},
volume = {4},
number = {4},
issn = {2378-962X},
url = {https://doi.org/10.1145/3375716},
doi = {10.1145/3375716},
abstract = {In this article, we make the case for the new class
of Self-aware Cyber-physical Systems. By bringing
together the two established fields of
cyber-physical systems and self-aware computing, we
aim at creating systems with strongly increased yet
managed autonomy, which is a main requirement for
many emerging and future applications and
technologies. Self-aware cyber-physical systems are
situated in a physical environment and constrained
in their resources, and they understand their own
state and environment and, based on that
understanding, are able to make decisions
autonomously at runtime in a self-explanatory
way. In an attempt to lay out a research agenda, we
bring up and elaborate on five key challenges for
future self-aware cyber-physical systems: (i) How
can we build resource-sensitive yet self-aware
systems? (ii) How to acknowledge situatedness and
subjectivity? (iii) What are effective
infrastructures for implementing self-awareness
processes? (iv) How can we verify self-aware
cyber-physical systems and, in particular, which
guarantees can we give? (v) What novel development
processes will be required to engineer self-aware
cyber-physical systems? We review each of these
challenges in some detail and emphasize that
addressing all of them requires the system to make a
comprehensive assessment of the situation and a
continual introspection of its own state to sensibly
balance diverse requirements, constraints,
short-term and long-term objectives. Throughout, we
draw on three examples of cyber-physical systems
that may benefit from self-awareness: a
multi-processor system-on-chip, a Mars rover, and an
implanted insulin pump. These three very different
systems nevertheless have similar characteristics:
limited resources, complex unforeseeable
environmental dynamics, high expectations on their
reliability, and substantial levels of risk
associated with malfunctioning. Using these
examples, we discuss the potential role of
self-awareness in both highly complex and rather
more simple systems, and as a main conclusion we
highlight the need for research on above listed
topics.},
month = {June},
articleno = {38},
numpages = {26}
}
@inproceedings{hanif:2019a,
author = {Muhammad Abdullah Hanif and Muhammad Zuhaib Akbar
and Rehan Ahmed and Semeen Rehman and Axel Jantsch
and Muhammad Shafique },
title = {{MemGANs}: Memory Management for Energy-Efficient
Acceleration of Complex Computations in Hardware
Architectures for Generative Adversarial Networks},
key = {ml},
booktitle = { Proceesings of the International Symposium on Low
Power Electronics and Design (ISLPED) },
year = 2019,
month = {July},
address = {Lausanne, Switzerland},
url = {http://jantsch.se/AxelJantsch/papers/2019/AbdullaHanif-ISLPED.pdf}
}
@inproceedings{taherinejad:2019a,
author = {Nima TaheriNejad and Axel Jantsch},
title = {Improved Machine Learning using Confidence},
key = {selfaware,ml},
booktitle = {IEEE Canadian Conference of Electrical and Computer
Engineering (CCECE)},
year = 2019,
month = {May},
address = {Edmonton, Canada},
url = {http://jantsch.se/AxelJantsch/papers/2019/NimaTaherinejad-CCECE.pdf}
}
@article{pagani:2018a,
author = {Santiago Pagani and Sai Manoj P D and Axel Jantsch
and J\"org Henkel},
title = {Machine Learning for Power, Energy, and Thermal
Management on Multi-core Processors: A Survey},
journal = {IEEE Transaction on Computer Aided Design (TCAD)},
year = 2018,
key = {ml,survey},
tudatabase = 1,
url = {http://jantsch.se/AxelJantsch/papers/2018/SantiagoPagani-TCAD-PowerManagementSurvey.pdf},
doi = {10.1109/TCAD.2018.2878168},
issn = {0278-0070}
}
@article{wess:2018a,
author = { Matthias Wess and Sai Manoj Pudukotai Dinakarrao
and Axel Jantsch},
title = {Weighted Quantization-Regularization in {DNNs} for
Weight Memory Minimization towards {HW}
Implementation },
journal = {IEEE Transactions on Computer-Aided Design of
Integrated Circuits and Systems },
year = 2018,
volume = 37,
number = 10,
month = {October},
key = {ml},
issn = {0278-0070},
doi = {10.1109/TCAD.2018.2857080},
tudatabase = 1,
url = {http://jantsch.se/AxelJantsch/papers/2018/MatthiasWess-CODES.pdf}
}