  Training deep learning models on embedded devices is typically avoided since
this requires more memory, computation and power over inference. In this work,
we focus on lowering the amount of memory needed for storing all activations,
which are required during the backward pass to compute the gradients. Instead,
during the forward pass, static Synthetic Gradient Modules (SGMs) predict
gradients for each layer. This allows training the model in a feed-forward
manner without having to store all activations. We tested our method on a robot
grasping scenario where a robot needs to learn to grasp new objects given only
a single demonstration. By first training the SGMs in a meta-learning manner on
a set of common objects, during fine-tuning, the SGMs provided the model with
accurate gradients to successfully learn to grasp new objects. We have shown
that our method has comparable results to using standard backpropagation.
