[AI_IMS_MED] Call flow for split inferencing loop
This contribution proposes a call flow for split inferencing operations between the UE and DCAS (Data Collection and Analytics Server), building upon previous work in TR 26.927 and earlier contributions.
The proposed call flow describes a collaborative inference execution model where:
- The UE and DCAS jointly execute an inference task
- The inference workload is split between the two entities
- Intermediate inference results are exchanged over the user plane
- Communication is facilitated through the MF (Media Function)
The text proposal adds the following procedural steps:
Parameters include tensor characteristics and compression profile identifiers
UE-Side Processing
UE generates intermediate data for continuation at DCAS
Data Exchange
UE transmits intermediate data to DCAS (via MF) according to configured format
DCAS-Side Processing
DCAS generates processed media data based on inference results
Result Delivery
This proposal enables distributed AI/ML inference for media processing, allowing workload distribution between device and network based on computational capabilities, latency requirements, and network conditions. The standardization of intermediate data format parameters ensures interoperability in split inference scenarios.