  5G and beyond cellular networks (NextG) will support the continuous execution
of resource-expensive edge-assisted deep learning (DL) tasks. To this end,
Radio Access Network (RAN) resources will need to be carefully "sliced" to
satisfy heterogeneous application requirements while minimizing RAN usage.
Existing slicing frameworks treat each DL task as equal and inflexibly define
the resources to assign to each task, which leads to sub-optimal performance.
In this paper, we propose SEM-O-RAN, the first semantic and flexible slicing
framework for NextG Open RANs. Our key intuition is that different DL
classifiers can tolerate different levels of image compression, due to the
semantic nature of the target classes. Therefore, compression can be
semantically applied so that the networking load can be minimized. Moreover,
flexibility allows SEM-O-RAN to consider multiple edge allocations leading to
the same task-related performance, which significantly improves system-wide
performance as more tasks can be allocated. First, we mathematically formulate
the Semantic Flexible Edge Slicing Problem (SF-ESP), demonstrate that it is
NP-hard, and provide an approximation algorithm to solve it efficiently. Then,
we evaluate the performance of SEM-O-RAN through extensive numerical analysis
with state-of-the-art multi-object detection (YOLOX) and image segmentation
(BiSeNet V2), as well as real-world experiments on the Colosseum testbed. Our
results show that SEM-O-RAN improves the number of allocated tasks by up to
169% with respect to the state of the art.
