Kevin Mika
PEVZ-ID
5 Publikationen
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2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982048Mika, K., Griessl, R., Kucza, N., Porrmann, F., Kaiser, M., Tigges, L., Hagemeyer, J., Trancoso, P., Azhar, M.W., Qararyah, F., Zouzoula, S., Ménétrey, J., Pasin, M., Felber, P., Marcus, C., Brunnegard, O., Eriksson, O., Salomonsson, H., Ödman, D., Ask, A., Casimiro, A., Bessani, A., Carvalho, T., Gugala, K., Zierhoffer, P., Latosinski, G., Tassemeier, M., Porrmann, M., Heyn, H.-M., Knauss, E., Mao, Y., Meierhöfer, F., Bartolini, A., Rietveld, K., Schuman, C., Moreira, J.: VEDLIoT. Next generation accelerated AIoT systems and applications. CF '23: Proceedings of the 20th ACM International Conference on Computing Frontiers. p. 291-296. ACM, New York, NY (2023).PUB | DOI
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2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2979799Griessl, R., Porrmann, F., Kucza, N., Mika, K., Hagemeyer, J., Kaiser, M., Porrmann, M., Tassemeier, M., Flottmann, M., Qararyah, F., Waqar, M., Trancoso, P., Ödman, D., Gugala, K., Latosinski, G.: Evaluation of heterogeneous AIoT Accelerators within VEDLIoT. 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE). p. 1-6. IEEE (2023).PUB | DOI
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2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2964196Kaiser, M., Griessl, R., Kucza, N., Haumann, C., Tigges, L., Mika, K., Hagemeyer, J., Porrmann, F., Rückert, U., 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.: VEDLIoT: Very Efficient Deep Learning in IoT. In: Institut of Electrical and Electronics Engineers (IEEE) (ed.) DATE '22: Proceedings of the 2022 Conference & Exhibition on Design, Automation & Test in Europe. p. 963-968. European Design and Automation Association, Leuven (2022).PUB | DOI
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2020 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982047Salami, B., Parasyris, K., Cristal, A., Unsal, O., Martorell, X., Carpenter, P., De La Cruz, R., Bautista, L., Jimenez, D., Alvarez, C., Nabavi, S., Madonar, S., Pericas, M., Trancoso, P., Abduljabbar, M., Chen, J., Soomro, P.N., Manivannan, M., Berge, M., Krupop, S., Klawonn, F., Mekhlafi, A., May, S., Becker, T., Gaydadjiev, G., Salomonsson, H., Dubhashi, D., Port, O., Etsion, Y., Quoc Do, L., Fetzer, C., Kaiser, M., Kucza, N., Hagemeyer, J., Griessl, R., Tigges, L., Mika, K., Huffmeier, A., Pasin, M., Schiavoni, V., Rocha, I., Gottel, C., Felber, P.: LEGaTO: Low-Energy, Secure, and Resilient Toolset for Heterogeneous Computing. 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). p. 169-174. IEEE, Piscataway, NJ (2020).PUB | DOI