Project A1 develops data analysis methods for embedded systems. Learning methods are to consume resources minimally so that they can be executed by embedded systems. A1 contributes to these overall goals by developing the theoretical basis of machine learning under resource constraints. In particular, it investigates machine learning models that formalise implicit functional relations within the data. More precisely, a model, e.g., from the class of linear models, exponential families, decision trees, or convolutional networks, specifies the general formal basis of any machine learning method. It determines implicitly what is learned (e.g., classifier, probability density, clustering model), and from what it is learned (e.g., real-valued vectors, categorical data, count data). Instances of a particular model are assessed by a quality measure (e.g., root mean square error, F β -score, correct classification rate, or the likelihood). In A1, we extend this notion of quality by adding simplicity of model representation and simplicity of inference, where simplicity is relative to the underlying hardware architecture. Thus, the quality of a model may comprise its memory consumption, numerical precision, parameter integrality, or approximability. Since learning itself is often expressed by one or more optimisation problems, resource constraints are added to the quality measure via regularisation. Several algorithms exist that can find (approximate) solutions to optimisation problems. Finally, the algorithms are executed on a specific hardware platform. Each of these layers, i.e., platforms, algorithms, models, is studied in the research centre. Project A3 starts with the execution on a certain platform, project A2 moves on to algorithms, and project A1 investigates the models of learning.
There is a plentitude of heuristics that algorithmically save resources. However, heuristics most often come without guarantees. In the A1 project, we specify restrictions at the model level and analyse the impact of the restriction on the quality. Moreover, a model of learning with proven guarantees makes explicit the link from the restrictions of the model to those of constrained hardware. It is a long way to achieve such models with guarantees. For the class of learning within the exponential family, we succeeded in developing spatiotemporal random fields, integer Markov random fields, and a novel quadrature based on first principles, each focusing on specific constraints. Proven properties of the novel models conclude the basic research on exponential families. At the same time, applications in other projects have already shown their practical usefulness. For top-down decision trees, the journey has just begun, and first publications show the potential of linking tree models of learning to FPGAs.
| Buschjaeger/etal/2021d |
Buschjäger, Sebastian and Hess, Sibylle and Morik, Katharina J..
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| Chen/etal/2022a |
Kuan-Hsun Chen and Chia-Hui Hsu and Christian Hakert and Sebastian Buschjäger and Chao-Lin Lee and Jenq-Kuen Lee and Katharina Morik and Jian-Jia Chen.
Efficient Realization of Decision Trees for Real-Time Inference.
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| Hakert/Chen/2022a |
Hakert, Christian and Chen, Kuan-Hsun and Chen, Jian-Jia.
Immediate Split Trees: Immediate Encoding of Floating Point Split Values in Random Forests.
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2022.
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| Hakert/Chen/2022b |
Hakert, Christian and Chen, Kuan-Hsun and Schirmeier, Horst and Chen, Jian-Jia and Genssler, Paul R. and von der Br\"uggen, Georg and Amrouch, Hussam and Henkel, J\"org and Chen, Jian-Jia.
Software-Managed Read and Write Wear-Leveling for Non-Volatile Main Memory.
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title = {Software-Managed Read and Write Wear-Leveling for Non-Volatile Main Memory}, journal = {ACM Transactions on Embedded Computing Systems}, year = {2022}, number = {1}, volume = {21}, url = {https://doi.org/10.1145/3483839}, abstract = {In-memory wear-leveling has become an important research field for emerging non-volatile main memories over the past years. Many approaches in the literature perform wear-leveling by making use of special hardware. Since most non-volatile memories only wear out from write accesses, the proposed approaches in the literature also usually try to spread write accesses widely over the entire memory space. Some non-volatile memories, however, also wear out from read accesses, because every read causes a consecutive write access. Software-based solutions only operate from the application or kernel level, where read and write accesses are realized with different instructions and semantics. Therefore different mechanisms are required to handle reads and writes on the software level. First, we design a method to approximate read and write accesses to the memory to allow aging aware coarse-grained wear-leveling in the absence of special hardware, providing the age information. Second, we provide specific solutions to resolve access hot-spots within the compiled program code (text segment) and on the application stack. In our evaluation, we estimate the cell age by counting the total amount of accesses per cell. The results show that employing all our methods improves the memory lifetime by up to a factor of 955x.} }')">
|
| Hakert/Khan/2022a |
Hakert, Christian and Khan, Asif-Ali and Chen, Kuan-Hsun and Hameed, Fazal and Castrillon, Jeronimo and Chen, Jian-Jia.
ROLLED: Racetrack Memory Optimized Linear Layout and Efficient Decomposition of Decision Trees.
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| Tsou/Chen/2022a |
Tsou, Yen-Ting and Chen, Kuan-Hsun and Yang, Chia-Lin and Cheng, Hsiang-Yun and Chen, Jian-Jia and Tsai, Der-Yu.
This is SPATEM! A Spatial-Temporal Optimization Framework for Efficient Inference on ReRAM-based CNN Accelerator.
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| Yayla/etal/2022a |
Mikail Yayla and Simon Thomann and Sebastian Buschjäger and Katharina Morik and Jian-Jia Chen and Hussam Amrouch.
Reliable Binarized Neural Networks on Unreliable Beyond von-Neumann Architecture.
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| Buschjaeger/etal/2021a |
Buschjäger, Sebastian and Chen, Jian-Jia and Chen, Kuan-Hsun and Günzel, Mario and Hakert, Christian and Morik, Katharina and Novkin, Rodion and Pfahler, Lukas and Yayla, Mikail.
Margin-Maximization in Binarized Neural Networks for Optimizing Bit Error Tolerance.
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| Buschjaeger/etal/2021b |
Buschjäger, Sebastian and Honysz, Philipp-Jan and Morik, Katharina.
Very Fast Streaming Submodular Function Maximization (Extended Version).
2021.
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| Buschjaeger/etal/2021c |
Sebastian Buschjäger and Jian-Jia Chen and Kuan-Hsun Chen and Mario Günzel and Katharina Morik and Rodion Novkin and Lukas Pfahler and Mikail Yayla.
Bit Error Tolerance Metrics for Binarized Neural Networks.
2021.
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| Buschjaeger/etal/2021e |
Buschjäger, Sebastian and Honysz, Philipp-Jan and Pfahler, Lukas and Morik, Katharina.
Very Fast Streaming Submodular Function Maximization.
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| Buschjaeger/Morik/2021a |
Sebastian Buschjäger and Katharina Morik.
There is no Double-Descent in Random Forests.
2021.
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| Buschjaeger/Morik/2021c |
Sebastian Buschjäger and Katharina Morik.
Improving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinement.
2021.
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| Chen/etal/2021a |
Chen, Jian-Jia and Huang, Wen-Hung and von der Brüggen, Georg and Ueter, Niklas.
On the Formalism and Properties of Timing Analyses in Real-Time Embedded Systems.
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Hakert, Christian and Khan, Asif Ali and Chen, Kuan-Hsun and Hameed, Fazal and Castrillon, Jeronimo and Chen, Jian-Jia.
BLOwing Trees to the Ground: Layout Optimization of Decision Trees on Racetrack Memory.
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| Hakert/etal/2021b |
Hakert, Christian and Kühn, Roland and Chen, Kuan-Hsun and Chen, Jian-Jia and Teubner, Jens.
OCTO+: Optimized Checkpointing of B+ Trees for Non-Volatile Main Memory Wear-Leveling.
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GPU-Accelerated Optimizer-Aware Evaluation of Submodular Exemplar Clustering.
2021.
}')">
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| Honysz/etal/2021b |
Honysz, Philipp-Jan and Schulze-Struchtrup, Alexander and Buschjäger, Sebastian and Morik, Katharina.
Providing Meaningful Data Summarizations Using Exemplar-based Clustering in Industry 4.0.
2021.
}')">
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| Shi/etal/2021a |
Shi, Junjie and Bian, Jiang and Richter, Jakob and Chen, Kuan-Hsun and Rahnenführer, Jörg and Xiong, Haoyi and Chen, Jian-Jia.
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FeFET-based Binarized Neural Networks Under Temperature-dependent Bit Errors.
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Binarized SNNs: Efficient and Error-Resilient Spiking Neural Networks through Binarization.
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On-Site Gamma-Hadron Separation with Deep Learning on FPGAs.
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Towards Explainable Bit Error Tolerance of Resistive RAM-Based Binarized Neural Networks.
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Sebastian Buschjäger and Lukas Pfahler and Katharina Morik.
Generalized Negative Correlation Learning for Deep Ensembling.
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Buschjäger, Sebastian and Honysz, Philipp-Jan and Morik, Katharina.
Randomized outlier detection with trees.
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Generalized Isolation Forest: Some Theory and More Applications -- Extended Abstract.
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Bucket of Ignorance: A Hybrid Data Structure for Timing Mechanism in Real-Time Operating Systems.
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Split’n Trace NVM: Leveraging Library OSes for Semantic Memory Tracing.
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| Nanni/etal/2020a |
Nanni, Mirco and Gennady, Andrienko and Barabasi, Albert-Laszlo and Boldrini, Chiara and Bonchi, Francesco and Cattuto, Ciro and Chiaromonte, Francesca and Commande, Giovanni and Conti, Marco and Cote, Mark and Dignum, Frank and Dignum, Virginia and Domingo-Ferrer, Josep and Ferragina, Paolo and Giannotti, Fosca and Guidotti, Riccardo and Helbng, Dirk and Kaski, Kimmo and Kertesz, Janos and Lehmann, Sune and Lepri, Bruno and Lukowicz, Paul and Matwin, Stan and Megias, Jimenez, David Megias and Monreale, Anna and Morik, Katharina and Oliver, Nuria and Passarella, Andrea and Passerini, Andrea and Pedreschi, Dino and Pentland, Alex and Pianesi, Fabio and Pratesi, Francesca and Rinzivillo, Salvatore and Ruggieri, Salvatore and Siebes, Arno and Torra, Vicenc and Trasarti, Roberto and van der Hoven, Jeroen and Vespignani, Alessandro.
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Fighting Filterbubbles with Adversarial Training.
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Lukas Pfahler and Jan Richter.
Interpretable Nearest Neighbor Queries for Tree-Structured Data in Vector Databases of Graph-Neural Network Embeddings.
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Offloading Safety- and Mission-Critical Tasks via Unreliable Connections.
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Using a Set of Triangle Inequalities to Accelerate K-means Clustering.
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Gaussian Model Trees for Traffic Imputation.
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The Trustworthy Pal: Controlling the False Discovery Rate in Boolean Matrix Factorization.
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