Pipelining on Edge Tensor Processing Units (TPUs) optimizes the deep neural network (DNN) inference by breaking it down into multiple stages processed concurrently on multiple accelerators. Such DNN inference tasks can be modeled as sporadic non-preemptive gangs with execution times that vary with their parallelism levels. This paper proposes a strict partitioning strategy for deploying DNN inferences in real-time systems. The strategy determines tasks' parallelism levels and assigns tasks to disjoint processor partitions. Configuring the tasks in the same partition with a uniform parallelism level avoids scheduling anomalies and enables schedulability verification using well-understood uniprocessor analyses. Evaluation using real-world Edge TPU benchmarks demonstrated that the proposed method achieves a higher schedulability ratio than state-of-the-art gang scheduling techniques.