  We present a library to automatically embed signal processing and neural
network predictions into the material robots are made of. Deep and shallow
neural network models are first trained offline using state-of-the-art machine
learning tools and then transferred onto general purpose microcontrollers that
are co-located with a robot's sensors and actuators. We validate this approach
using multiple examples: a smart robotic tire for terrain classification, a
robotic finger sensor for load classification and a smart composite capable of
regressing impact source localization. In each example, sensing and computation
are embedded inside the material, creating artifacts that serve as stand-in
replacement for otherwise inert conventional parts. The open source software
library takes as inputs trained model files from higher level learning
software, such as Tensorflow/Keras, and outputs code that is readable in a
microcontroller that supports C. We compare the performance of this approach
for various embedded platforms. In particular, we show that low-cost
off-the-shelf microcontrollers can match the accuracy of a desktop computer,
while being fast enough for real-time applications at different neural network
configurations. We provide means to estimate the maximum number of parameters
that the hardware will support based on the microcontroller's specifications.
