TF2AIF: Facilitating development and deployment of accelerated AI models on the cloud-edge continuum
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914882254798848 |
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| author | Leftheriotis, Aimilios Tzenetopoulos, Achilleas Lentaris, George Soudris, Dimitrios Theodoridis, Georgios |
| author_facet | Leftheriotis, Aimilios Tzenetopoulos, Achilleas Lentaris, George Soudris, Dimitrios Theodoridis, Georgios |
| contents | The B5G/6G evolution relies on connect-compute technologies and highly heterogeneous clusters with HW accelerators, which require specialized coding to be efficiently utilized. The current paper proposes a custom tool for generating multiple SW versions of a certain AI function input in high-level language, e.g., Python TensorFlow, while targeting multiple diverse HW+SW platforms. TF2AIF builds upon disparate tool-flows to create a plethora of relative containers and enable the system orchestrator to deploy the requested function on any peculiar node in the cloud-edge continuum, i.e., to leverage the performance/energy benefits of the underlying HW upon any circumstances. TF2AIF fills an identified gap in today's ecosystem and facilitates research on resource management or automated operations, by demanding minimal time or expertise from users. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13715 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TF2AIF: Facilitating development and deployment of accelerated AI models on the cloud-edge continuum Leftheriotis, Aimilios Tzenetopoulos, Achilleas Lentaris, George Soudris, Dimitrios Theodoridis, Georgios Machine Learning The B5G/6G evolution relies on connect-compute technologies and highly heterogeneous clusters with HW accelerators, which require specialized coding to be efficiently utilized. The current paper proposes a custom tool for generating multiple SW versions of a certain AI function input in high-level language, e.g., Python TensorFlow, while targeting multiple diverse HW+SW platforms. TF2AIF builds upon disparate tool-flows to create a plethora of relative containers and enable the system orchestrator to deploy the requested function on any peculiar node in the cloud-edge continuum, i.e., to leverage the performance/energy benefits of the underlying HW upon any circumstances. TF2AIF fills an identified gap in today's ecosystem and facilitates research on resource management or automated operations, by demanding minimal time or expertise from users. |
| title | TF2AIF: Facilitating development and deployment of accelerated AI models on the cloud-edge continuum |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.13715 |