M.Sc. Javier Hoffmann
Verfügungsgebäude 1C, Raum 1.40

T: +49 (0) 355 69 2052
F: +49 (0) 355 69 2027

Scientific Assistant

Research Areas

Scientific Interests:

  • Scheduling
    • Time Criticality
    • Critical Instant
    • Algorithms
  • Energy efficiency
  • Artificial Intelligence
    • Machine Learning
      • Neural Networks
        • CNN
        • RNN
  • Internet of Things
    • Wireless Communication
    • Protocols
  • Functional Safety
    • Integrity
    • Availability
    • Single Point of Failure 

Themes for Student Thesis: 

Topic 1 – Medical Care Task Scheduling

Implementation of an ECG algorithm utilizing a Microcontroller and a set of sensors. 

The objective of this Thesis is to determine the capabilities of a scheduling algorithm in the medical field. As there are several items to be considered, including criticality of the tasks, energy available and human conditions, it is important to analyze the timely and correct execution of the system. 

We are looking for a responsible and proactive student who is willing to work, according to its experience level (Bachelor / Master), on an embedded System: M5Stack programmed with C/C++  

Topic 2 – Scheduler Analysis

Analysis of Parameters for a Scheduling Algorithm 

We are performing some tests in the scheduling area and developed a dynamic scheduling algorithm which could be used for a variety of scenarios. The problem is that we require the fine tuning of the parameters present in the algorithm.  

The student (Bachelor/Master) will learn about scheduling algorithms, optimization and parameter selection over different techniques. The algorithm and test environment were created using C/C++, Matlab and FreeRTOS 

Topic 3 – Energy Testing and Battery Analysis for a Multi-Criticality Scenario

Analysis of energy consumption and battery life in a multi-criticality tasks scenario 

Our approach on a novel scheduler has proven to be energy efficient, however, we need to obtain more information to be sure that the low energy consumption is applicable on a variety of use cases. Therefore, it is necessary to identify, define, test and analyze the energy consumption in different configurations 

The student (Bachelor, Master) should be interested in the study of energy consumption, furthermore, knowledge in C/C++ are beneficial  


[2020] Osvaldo Navarro, Jones Yudi Mori, Javier Hoffmann, Hector Gerardo Muñoz Hernandez, Michael Hübner:A Machine Learning Methodology for Cache Memory Design Based on Dynamic Instructions. ACM Trans. Embedded Comput. Syst. 19(2): 12:1-12:20 (2020) 

[2020] Fricke, Florian; Mahmood, Safdar; Hoffmann, Javier; Ali, Muhammad; Shahin, Keyvan; Hübner, Michael; Göhringer, Diana: (2020) Domain Adaptive Processor Architectures. In: Jasperneite J., Lohweg V. (eds) Kommunikation und Bildverarbeitung in der Automation. Technologien für die intelligente Automation (Technologies for Intelligent Automation), vol 12. Springer Vieweg, Berlin, Heidelberg 

[2019] Flo­ri­an Fri­cke, Ja­vier Edu­ar­do Hoff­mann, Saf­dar Mah­mood, Micha­el Hüb­ner - “A Tool to Ease De­sign-Space-Ex­plo­ra­ti­on Using the Ten­si­li­ca LX7 ASIP” in CDN­Li­ve EMEA 2019 

[2019] J. Hoffmann: “Energy Aware Enhanced Earliest Deadline First Algorithm”. In: K. De Bosschere, Ed., “ACACES 2019: poster abstracts.” HiPEAC High-Performance Embedded Architecture and Compilation, Subiaco, Rome, 2019. 

[2018] J. Hoffmann, D. Kuschnerus, T. Jones and M. Hübner, "Towards a Safety and Energy Aware protocol for Wireless Communication," 2018 13th International Symposium on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC), Lille, 2018, pp. 1-6. 

[2018] J. Hoffmann: “An Energy-aware Dynamic Scheduler for Hard Deadline Tasks”. In: K. De Bosschere, Ed., “ACACES 2018: poster abstracts.” HiPEAC High-Performance Embedded Architecture and Compilation, Subiaco, Rome, 2018. 

[2017] Flo­ri­an Käs­t­ner, Os­val­do Na­var­ro Guz­man, Be­ne­dikt Jan­ßen, Ja­vier Edu­ar­do Hoff­mann, Micha­el Hüb­ner. Ana­ly­sis of Hard­ware Im­ple­men­ta­ti­ons to Ac­ce­le­ra­te Con­vo­lu­tio­nal and Re­cur­rent Neu­ro­nal Net­works. In In­ter­na­tio­nal Jour­nal on Ad­van­ces in Soft­ware, vol 10 no 3 & 4, year 2017 

[2017] Ja­vier Edu­ar­do Hoff­mann, Os­val­do Na­var­ro Guz­man, Flo­ri­an Käs­t­ner, Be­ne­dikt Jan­ßen, Micha­el Hüb­ner, “A survey on CNN and RNN implementations,” in PE­SA­RO 2017, The Seventh In­ter­na­tio­nal Con­fe­rence on Per­for­mance, Sa­fe­ty and Ro­bust­ness in Com­plex Sys­tems and Ap­p­li­ca­ti­ons, 2017 

[2017] Osvaldo Navarro, Jones Mori, Javier Hoffmann, Fabian Stuckmann, and Michael Hübner. 2017. A machine learning methodology for cache recommendation. In Proceedings of the International Symposium on Applied Reconfigurable Computing. Springer, 311--322. 

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