Profiling Neural Network Partitioning Strategies for Inference across the Computing Continuum
A formal model that unifies functional, pipelined, and data-parallel partitioning strategies within a single abstraction over heterogeneous CC topologies, enabling structured cross-strategy comparison and enabling empirical, cross-strategy comparison of distributed inference deployments is introduced.