Limited resources in embedded devices often hinder the execution of computation-heavy machine learning processes. Running deep neural network (DNN) workloads while preserving the integrity of the model parameters and without compromising temporal constraints of real-time applications, is a challenging problem. Although secure enclaves such as ARM TrustZone can ensure the integrity of applications, off-the-shelf implementations are often infeasible for DNN workloads - especially those with real-time requirements - due to additional resource and temporal constraints. This paper presents a real-time scheduling framework that enables the execution of resource-intensive DNN workloads inside TrustZone-enabled secure enclaves. Our approach reduces the resource overhead by fusing multiple layers of multiple tasks and running them all together inside the enclaves while retaining real-time grantees. We derive mathematical conditions that will allow the designer to test the feasibility of deploying DNN workload in a TrustZone-enabled system. Our comparisons with a standard fixed-priority real-time scheduler show that we can schedule up to 21.33% more tasksets in higher utilization (e.g., > 80%) scenarios.