Search Results - "Harbaum, Tanja"

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  1. 1

    CNNParted: An open source framework for efficient Convolutional Neural Network inference partitioning in embedded systems by Kreß, Fabian, Sidorenko, Vladimir, Schmidt, Patrick, Hoefer, Julian, Hotfilter, Tim, Walter, Iris, Harbaum, Tanja, Becker, Jürgen

    “…Applications such as autonomous driving or assistive robotics heavily rely on the usage of Deep Neural Networks. In particular, Convolutional Neural Networks…”
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    Journal Article
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    Design Space Exploration on Efficient and Accurate Human Pose Estimation from Sparse IMU-Sensing by Furst-Walter, Iris, Nappi, Antonio, Harbaum, Tanja, Becker, Jurgen

    “…Human Pose Estimation (HPE) to assess human motion in sports, rehabilitation or work safety requires accurate sensing without compromising the sensitive…”
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    Conference Proceeding
  3. 3

    A Challenge-Based Blended Learning Approach for an Introductory Digital Circuits and Systems Course by Hoefer, Julian, Gaus, Michael, Adams, Manuela, Kres, Fabian, Kempf, Fabian, Karle, Christian, Harbaum, Tanja, Barth, Andreas, Becker, Juergen

    “…In the early stages of university education, frontal teaching within expansive lecture halls and paper-based assignments predominate. Students often encounter…”
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    Conference Proceeding
  4. 4

    KIHT: Kaligo-Based Intelligent Handwriting Teacher by Harbaum, Tanja, Serdyuk, Alexey, Kreb, Fabian, Hamann, Tim, Barth, Jens, Kampf, Peter, Imbert, Florent, Soullard, Yann, Tavenard, Romain, Anquetil, Eric, Delahaie, Jessica

    “…Kaligo-based Intelligent Handwriting Teacher (KIHT) is a bi-nationally funded research project. The aim of this joint project is to develop an intelligent…”
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    Conference Proceeding
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    Towards a General Compilation Approach for On-device Training in Embedded Systems by Topko, Iuliia, Harbaum, Tanja, Becker, Juergen

    “…Machine learning (ML) compilers become essential for the deployment process on hardware with the increasing application of artificial intelligence algorithms…”
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    Conference Proceeding
  9. 9

    ICE TEA: Insertion of Custom Early Exits for Time-, Energy- & Anomaly-Aware Neural Networks by Stammler, Matthias, Hoefer, Julian, Schmidt, Patrick, Harbaum, Tanja, Becker, Jurgen

    “…Integrating neural network inference in systems with variable execution contexts proves difficult due to the difficulty in predicting runtime and energy…”
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    Conference Proceeding
  10. 10

    Design Space Exploration on Efficient and Accurate Human Pose Estimation from Sparse IMU-Sensing by Fürst-Walter, Iris, Nappi, Antonio, Harbaum, Tanja, Becker, Jürgen

    Published 12-02-2024
    “…Human Pose Estimation (HPE) to assess human motion in sports, rehabilitation or work safety requires accurate sensing without compromising the sensitive…”
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    Journal Article
  11. 11

    Mitigating Masking in Automotive Communication Systems: Modeling and Hardware Generation by Stammler, Matthias, Hamann, Matthias, Harbaum, Tanja, Becker, Jurgen

    “…The development of self-driving cars and driver-assistance systems necessitates highly interconnected system architectures with increasing communication volume…”
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    Conference Proceeding
  12. 12

    Approximate Accelerators: A Case Study using Runtime Reconfigurable Processors by Lesniak, Fabian, Harbaum, Tanja, Becker, Jurgen

    “…Dennard Scaling and Amdahl's Law have ensured the continuous improvement of computing architectures for years, but even after they fade, the demand for…”
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    Conference Proceeding
  13. 13

    DREAM: Distributed Reinforcement Learning Enabled Adaptive Mixed-Critical NoC by Anantharajaiah, Nidhi, Xu, Yunhe, Lesniak, Fabian, Harbaum, Tanja, Becker, Juergen

    “…Applications of different criticality sharing the same System-on-Chip (SoC) platform are increasing in popularity to reduce overall cost. Spatial and temporal…”
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    Conference Proceeding
  14. 14

    BayWatch: Leveraging Bayesian Neural Networks for Hardware Fault Tolerance and Monitoring by Hoefer, Julian, Stammler, Matthias, KreB, Fabian, Hotfilter, Tim, Harbaum, Tanja, Becker, Juergen

    “…As Deep Neural Networks are increasingly being utilized in safety-critical domains, assessing the uncertainty of the models during inference will be a crucial…”
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    Conference Proceeding
  15. 15

    RVVe: A Minimal RISC-V Vector Processor for Embedded AI Acceleration by Schmidt, Patrick, Pfau, Johannes, Hotfilter, Tim, Stammler, Matthias, Harbaum, Tanja, Becker, Jurgen

    “…In recent years, Deep Neural Networks (DNNs) have become common in the embedded domain for a wide variety of tasks. However, due to strict area and energy…”
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    Conference Proceeding
  16. 16

    LOTTA: An FPGA-based Low-Power Temporal Convolutional Network Hardware Accelerator by Kres, Fabian, Serdyuk, Alexey, Kobsar, Denis, Hotfilter, Tim, Hoefer, Julian, Harbaum, Tanja, Becker, Jurgen

    “…Deploying artificial intelligence (AI) in the Internet of Things (IoT) is primarily limited by energy consumption. Optimized hardware architectures are…”
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    Conference Proceeding
  17. 17

    Modular Hardware Design for High-Performance MIMO-Capable SDR Systems to Accelerate 6G Development by Karle, Christian, Neu, Marc, Nuss, Benjamin, Chen, Jiayi, Witte, Lukas, Scheder, Andre, Harbaum, Tanja, Becker, Jurgen

    “…Compared to 5th generation (5G), 6G is expected to offer latencies below 1 ms, higher data rates in the range of multiple Gbit/s, and enable the use of new…”
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    Conference Proceeding
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    A Dynamically Pipelined Dataflow Architecture for Graph Convolutions in Real-Time Event Interpretation by Neu, Marc, Karle, Christian, Schmidt, Patrick, Hofer, Julian, Harbaum, Tanja, Becker, Jurgen

    “…In high-performance data acquisition systems, realtime interpretation of captured signals is crucial due to limited offline storage and processing…”
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    Conference Proceeding
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    On Metric-Driven Development of Embedded Neuromorphic AI by Krausse, Jann, Neher, Moritz, Fuerst-Walter, Iris, Weigelt, Carmen, Harbaum, Tanja, Knobloch, Klaus, Becker, Juergen

    “…Neuromorphic computing presents a promising approach to embedded artificial intelligence (AI) by deploying spiking neural networks (SNNs) to specialized…”
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    Conference Proceeding
  20. 20

    VHDL Crash Course: A Multimedia-Based Teaching Approach by KreB, Fabian, Sidorenko, Vladimir, Topko, Iuliia, Unger, Kai, Harbaum, Tanja, Becker, Jurgen

    “…Gaining a solid understanding of hardware description is crucial for electrical engineering students, as it enables them to understand and design digital…”
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    Conference Proceeding