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.

通过创建多个高级语言版本的某个 AI 函数输入，以适应多种不同的硬软件平台，TF2AIF 填补了当前生态系统中的一个空白，并通过要求用户最少的时间或专业知识，促进资源管理或自动化操作的研究。