A scalable, heterogeneous hardware platform for accelerated aiot based on microservers
| Autor(en): | Griessl, 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. |
Stichwörter: | (far) edge-computing; Accelerator; AIoT; Deep learning; Energy-efficiency; FPGA; IoT; Machine learning; Microserver; Performance classification | Erscheinungsdatum: | 2023 | Herausgeber: | River Publishers | Enthalten in: | Shaping the Future of IoT with Edge Intelligence: How Edge Computing Enables the Next Generation of IoT Applications | Startseite: | 179 – 196 | Zusammenfassung: | Performance and energy efficiency are key aspects of next-generation AIoT hardware. This chapter presents a scalable, heterogeneous hardware platform for accelerated AIoT based on microserver technology. It integrates several accelerator platforms based on technologies like CPUs, embedded GPUs, FPGAs, or specialized ASICs, supporting the full range of the cloud-edge-IoT continuum. The modular microserver approach enables the integration of different, heterogeneous accelerators into one platform. Benchmarking the various accelerators takes performance, energy efficiency, and accuracy into account. The results provide a solid overview of available accelerator solutions and guide hardware selection for AIoT applications from the far edge to the cloud. © The Editor(s) (if applicable) and The Author(s) 2023. All rights reserved. |
Beschreibung: | Cited by: 0 |
ISBN: | 9788770040266 9788770040273 |
Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173023396&partnerID=40&md5=801665a7503d8e93263e7f1c77ce52e0 |
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