Embedding neural networks in ultra-resource-constrained, low-cost, off-the-shelf microcontrollers promotes the widespread adoption of the Internet of Intelligent Things. A promising approach involves mapping network parameters from real to either binary or ternary values to reduce computational workload. Hence, we introduce Drupelet, a novel software framework for the development of mixed-precision (binary and ternary) neural networks for ultra-resource-constrained embedded devices. The modularity of Drupelet provides ease of maintenance, debugging and reuse. Drupelet was used to define the edges of memory and inference time of typical layers on two different microcontrollers. The results demonstrate that Drupelet enables mass deployment of IoIT systems, thanks to the low cost of ultra resource-constrained microcontrollers.