We present TaDA, a system architecture enabling efficient execution of Internet of Things (IoT) applications across multiple computing units, powered by ambient energy harvesting. Low-power microcontroller units (MCUs) are increasingly specialized; for example, custom designs feature hardware acceleration of neural network inference, next to designs providing energy-efficient input/output. As application requirements are growingly diverse, we argue that no single MCU can efficiently fulfill them. TaDA allows programmers to assign the execution of different slices of the application logic to the most efficient MCU for the job. We achieve this by decoupling task executions in time and space, using a special-purpose hardware interconnect we design, while providing persistent storage to cross periods of energy unavailability. We compare our prototype performance against the single most efficient computing unit for a given workload. We show that our prototype saves up to 96.7% energy per application round. Given the same energy budget, this yields up to a 68.7x throughput improvement.