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To keep up with the ever-growing performance demand of neural networks, specialized hardware (HW) accelerators are shifting towards multi-core and chiplet architectures. So far, these multi-accelerator systems exploit the increased parallelism by pipelining different NN layers across input batches on different cores to increase throughput. Yet, when pursuing this with non-batched layer-by-layer scheduling of latency-critical applications, this fails to fully exploit the available HW resources towards energy-efficient execution at the edge. This work, therefore, enables fine-grained depth-first scheduling of layer-fused DNNs onto multi-core architectures through an open-source modeling framework called Stream. Stream is capable of representing a wide range of scheduling granularities and HW architectures and optimizes execution schedules towards minimal energy, minimal latency and/or minimal memory footprint for constrained edge devices. We validate against three SotA HW implementations employing layer-fused scheduling showing tight matching with measured efficiencies. Using Stream in further explorations, we demonstrate that high-level architectural decisions greatly impact hardware efficiency under the fine-grained scheduling paradigm, reducing the energy-delay product from 2.4x for single-core architectures to up to 30x for heterogeneous multi-core architectures compared to the traditional scheduling at layer granularity.