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4 results on '"Tassemeier, M."'

1. A Scalable, Heterogeneous Hardware Platform for Accelerated AIoT based on Microservers

Author

Griessl, R., primary, Porrmann, F., additional, Kucza, N., additional, Mika, K., additional, Hagemeyer, J., additional, Kaiser, M., additional, Porrmann, M., additional, Tassemeier, M., additional, Flottmann, M., additional, Qararyah, F., additional, Waqar, M., additional, Trancoso, P., additional, Ödman, D., additional, Gugala, K., additional, and Latosinski, G., additional

Published
2023
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2. Evaluation of heterogeneous AIoT Accelerators within VEDLIoT

Author

Griessl, René, Porrmann, Florian, Kucza, Nils, Mika, Kevin, Hagemeyer, Jens, Kaiser, Martin, Porrmann, M., Tassemeier, M., Flottmann, M., Qararyah, F., Waqar, M., Trancoso, P., Ödman, D., Gugala, K., and Latosinski, G.

Abstract

Within VEDLIoT, a project targeting the development of energy-efficient Deep Learning for distributed AIoT applications, several accelerator platforms based on technologies like CPUs, embedded GPUs, FPGAs, or specialized ASICs are evaluated. The VEDLIoT approach is based on modular and scalable cognitive IoT hardware platforms. Modular microserver technology enables the integration of different, heterogeneous accelerators into one platform. Benchmarking of the different accelerators takes into account performance, energy efficiency and accuracy. The results in this paper provide a solid overview regarding available accelerator solutions and provide guidance for hardware selection for AIoT applications from far edge to cloud. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage. The focus is on the considerations of the performance and energy efficiency of hardware accelerators. Apart from the hardware and accelerator focus presented in this paper, the project also covers toolchain, security and safety aspects. The resulting technology is tested on a wide range of AIoT applications.

Published
2023

3. VEDLIoT: Very Efficient Deep Learning in IoT

Author

Kaiser, M., primary, Griessl, R., additional, Kucza, N., additional, Haumann, C., additional, Tigges, L., additional, Mika, K., additional, Hagemeyer, J., additional, Porrmann, F., additional, Ruckert, U., additional, vor dem Berge, M., additional, Krupop, S., additional, Porrmann, M., additional, Tassemeier, M., additional, Trancoso, P., additional, Qararyah, F., additional, Zouzoula, S., additional, Casimiro, A., additional, Bessani, A., additional, Cecilio, J., additional, Andersson, S., additional, Brunnegard, O., additional, Eriksson, O., additional, Weiss, R., additional, Mcierhofer, F., additional, Salomonsson, H., additional, Malekzadeh, E., additional, Odman, D., additional, Khurshid, A., additional, Felber, P., additional, Pasin, M., additional, Schiavoni, V., additional, Menetrey, J., additional, Gugala, K., additional, Zierhoffer, P., additional, Knauss, E., additional, and Heyn, H., additional

Published
2022
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4. VEDLIoT: Very Efficient Deep Learning in IoT

Author

Kaiser, Martin, Griessl, René, Kucza, Nils, Haumann, Carola, Tigges, Lennart, Mika, Kevin, Hagemeyer, Jens, Porrmann, Florian, Rückert, Ulrich, vor dem Berge, M., Krupop, S., Porrmann, M., Tassemeier, M., Trancoso, P., Qararyah, F., Zouzoula, S., Casimiro, A., Bessani, A., Cecilio, J., Andersson, S., Brunnegard, O., Eriksson, O., Weiss, R., Mcierhofer, F., Salomonsson, H., Malekzadeh, E., Odman, D., Khurshid, A., Felber, P., Pasin, M., Schiavoni, V., Menetrey, J., Gugala, K., Zierhoffer, P., Knauss, E., Heyn, H., and ARRAY(0xab8ee90)

Subjects
Performance (cs.PF), FOS: Computer and information sciences, Computer Science - Cryptography and Security, Computer Science - Performance, Computer Science - Distributed, Parallel, and Cluster Computing, Hardware Architecture (cs.AR), Distributed, Parallel, and Cluster Computing (cs.DC), Computer Science - Hardware Architecture, Cryptography and Security (cs.CR)
Abstract

The VEDLIoT project targets the development of energy-efficient Deep Learning for distributed AIoT applications. A holistic approach is used to optimize algorithms while also dealing with safety and security challenges. The approach is based on a modular and scalable cognitive IoT hardware platform. Using modular microserver technology enables the user to configure the hardware to satisfy a wide range of applications. VEDLIoT offers a complete design flow for Next-Generation IoT devices required for collaboratively solving complex Deep Learning applications across distributed systems. The methods are tested on various use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage with the first results available., Comment: This publication incorporates results from the VEDLIoT project, which received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 957197

Published
2022

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