@inbook{45899, author = {{Boschmann, Alexander and Clausing, Lennart and Jentzsch, Felix and Ghasemzadeh Mohammadi, Hassan and Platzner, Marco}}, booktitle = {{On-The-Fly Computing -- Individualized IT-services in dynamic markets}}, editor = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}}, pages = {{225--236}}, publisher = {{Heinz Nixdorf Institut, Universität Paderborn}}, title = {{{Flexible Industrial Analytics on Reconfigurable Systems-On-Chip}}}, doi = {{10.5281/zenodo.8068713}}, volume = {{412}}, year = {{2023}}, } @misc{45917, author = {{Raeisi Nafchi, Masood}}, publisher = {{Paderborn University}}, title = {{{Reconfigurable Random Forest Implementation on FPGA}}}, year = {{2023}}, } @misc{45916, author = {{Yadalam Murali Kumar, Nihal}}, publisher = {{Paderborn University}}, title = {{{Data Analytics for Predictive Maintenance of Time Series Data}}}, year = {{2023}}, } @inproceedings{45913, author = {{Clausing, Lennart and Guetattfi, Zakarya and Kaufmann, Paul and Lienen, Christian and Platzner, Marco}}, booktitle = {{Proceedings of the 19th International Symposium on Applied Reconfigurable Computing (ARC)}}, title = {{{On Guaranteeing Schedulability of Periodic Real-time Hardware Tasks under ReconOS64}}}, year = {{2023}}, } @book{45863, abstract = {{In the proposal for our CRC in 2011, we formulated a vision of markets for IT services that describes an approach to the provision of such services that was novel at that time and, to a large extent, remains so today: „Our vision of on-the-fly computing is that of IT services individually and automatically configured and brought to execution from flexibly combinable services traded on markets. At the same time, we aim at organizing markets whose participants maintain a lively market of services through appropriate entrepreneurial actions.“ Over the last 12 years, we have developed methods and techniques to address problems critical to the convenient, efficient, and secure use of on-the-fly computing. Among other things, we have made the description of services more convenient by allowing natural language input, increased the quality of configured services through (natural language) interaction and more efficient configuration processes and analysis procedures, made the quality of (the products of) providers in the marketplace transparent through reputation systems, and increased the resource efficiency of execution through reconfigurable heterogeneous computing nodes and an integrated treatment of service description and configuration. We have also developed network infrastructures that have a high degree of adaptivity, scalability, efficiency, and reliability, and provide cryptographic guarantees of anonymity and security for market participants and their products and services. To demonstrate the pervasiveness of the OTF computing approach, we have implemented a proof-of-concept for OTF computing that can run typical scenarios of an OTF market. We illustrated the approach using a cutting-edge application scenario – automated machine learning (AutoML). Finally, we have been pushing our work for the perpetuation of On-The-Fly Computing beyond the SFB and sharing the expertise gained in the SFB in events with industry partners as well as transfer projects. This work required a broad spectrum of expertise. Computer scientists and economists with research interests such as computer networks and distributed algorithms, security and cryptography, software engineering and verification, configuration and machine learning, computer engineering and HPC, microeconomics and game theory, business informatics and management have successfully collaborated here.}}, author = {{Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}}, pages = {{247}}, publisher = {{Heinz Nixdorf Institut, Universität Paderborn}}, title = {{{On-The-Fly Computing -- Individualized IT-services in dynamic markets}}}, doi = {{10.17619/UNIPB/1-1797}}, volume = {{412}}, year = {{2023}}, } @inproceedings{32855, author = {{Clausing, Lennart and Platzner, Marco}}, booktitle = {{2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)}}, location = {{ Lyon, France}}, pages = {{120--127}}, publisher = {{IEEE}}, title = {{{ReconOS64: A Hardware Operating System for Modern Platform FPGAs with 64-Bit Support}}}, doi = {{10.1109/ipdpsw55747.2022.00029}}, year = {{2022}}, } @misc{45715, author = {{Tcheussi Ngayap, Vanessa Ingrid}}, title = {{{FreeRTOS on a MicroBlaze Soft-Core Processor with Hardware Accelerators}}}, year = {{2022}}, } @misc{45914, author = {{Manjunatha, Suraj}}, publisher = {{Paderborn University }}, title = {{{Dealing With Pre-Processing And Feature Extraction Of Time-Series Data In Predictive Maintenance}}}, year = {{2022}}, } @misc{45915, author = {{Kaur , Parvinder}}, title = {{{Analysis of Time-Series Classification in Conditional Monitoring Systems}}}, year = {{2022}}, } @inproceedings{30909, author = {{Clausing, Lennart}}, booktitle = {{Proceedings of the 11th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies}}, publisher = {{ACM}}, title = {{{ReconOS64: High-Performance Embedded Computing for Industrial Analytics on a Reconfigurable System-on-Chip}}}, doi = {{10.1145/3468044.3468056}}, year = {{2021}}, } @inproceedings{30908, author = {{Ghasemzadeh Mohammadi, Hassan and Jentzsch, Felix and Kuschel, Maurice and Arshad, Rahil and Rautmare, Sneha and Manjunatha, Suraj and Platzner, Marco and Boschmann, Alexander and Schollbach, Dirk }}, booktitle = {{ Machine Learning and Principles and Practice of Knowledge Discovery in Databases}}, publisher = {{Springer}}, title = {{{FLight: FPGA Acceleration of Lightweight DNN Model Inference in Industrial Analytics}}}, doi = {{https://doi.org/10.1007/978-3-030-93736-2_27}}, year = {{2021}}, } @misc{20820, author = {{Thiele, Simon}}, title = {{{Implementing Machine Learning Functions as PYNQ FPGA Overlays}}}, year = {{2020}}, } @misc{20821, author = {{Jaganath, Vivek}}, title = {{{Extension and Evaluation of Python-based High-Level Synthesis Tool Flows}}}, year = {{2020}}, } @article{13770, author = {{Karl, Holger and Kundisch, Dennis and Meyer auf der Heide, Friedhelm and Wehrheim, Heike}}, journal = {{Business & Information Systems Engineering}}, number = {{6}}, pages = {{467--481}}, publisher = {{Springer}}, title = {{{A Case for a New IT Ecosystem: On-The-Fly Computing}}}, doi = {{10.1007/s12599-019-00627-x}}, volume = {{62}}, year = {{2020}}, } @inproceedings{20808, author = {{Ghasemzadeh Mohammadi, Hassan and Arshad, Rahil and Rautmare, Sneha and Manjunatha, Suraj and Kuschel, Maurice and Jentzsch, Felix Paul and Platzner, Marco and Boschmann, Alexander and Schollbach, Dirk}}, booktitle = {{2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}}, isbn = {{9781728189567}}, title = {{{DeepWind: An Accurate Wind Turbine Condition Monitoring Framework via Deep Learning on Embedded Platforms}}}, doi = {{10.1109/etfa46521.2020.9211880}}, year = {{2020}}, } @misc{21433, abstract = {{Modern machine learning (ML) techniques continue to move into the embedded system space because traditional centralized compute resources do not suit certain application domains, for example in mobile or real-time environments. Google’s TensorFlow Lite (TFLite) framework supports this shift from cloud to edge computing and makes ML inference accessible on resource-constrained devices. While it offers the possibility to partially delegate computation to hardware accelerators, there is no such “delegate” available to utilize the promising characteristics of reconfigurable hardware. This thesis incorporates modern platform FPGAs into TFLite by implementing a modular delegate framework, which allows accelerators within the programmable logic to take over the execution of neural network layers. To facilitate the necessary hardware/software codesign, the FPGA delegate is based on the operating system for reconfigurable computing (ReconOS), whose partial reconfiguration support enables the instantiation of model-tailored accelerator architectures. In the hardware back-end, a streaming-based prototype accelerator for the MobileNet model family showcases the working order of the platform, but falls short of the desired performance. Thus, it indicates the need for further exploration of alternative accelerator designs, which the delegate could automatically synthesize to meet a model’s demands.}}, author = {{Jentzsch, Felix P.}}, title = {{{Design and Implementation of a ReconOS-based TensorFlow Lite Delegate Architecture}}}, year = {{2020}}, } @misc{15874, author = {{Lienen, Christian}}, publisher = {{Universität Paderborn}}, title = {{{Implementing a Real-time System on a Platform FPGA operated with ReconOS}}}, year = {{2019}}, }