The performance of software algorithms can be improved by performing those algorithms on specialized embedded hardware. However, complex algorithms that rely on input data at runtime for configuration have a combinatorial explosion of possible configurations, which has historically put hardware acceleration out of reach for applications wishing to serve large configuration spaces. Data adaptable embedded systems overcome this limitation by allowing for hardware reconfiguration during runtime, but the complexity of the specification of these systems is difficult to manage with traditional techniques. In this paper, a modeling approach is discussed in order to concurrently model two aspects of the final system: dependencies between algorithm tasks, and desired hardware configurations for each task. The contribution of the work is the model-based generation of hardware and software tasks, as well as a control scheme customized to each model that oversees the dynamic reconfiguration process.