  On-board processing elements on UAVs are currently inadequate for training
and inference of Deep Neural Networks. This is largely due to the energy
consumption of memory accesses in such a network. HadaNets introduce a flexible
train-from-scratch tensor quantization scheme by pairing a full precision
tensor to a binary tensor in the form of a Hadamard product. Unlike wider
reduced precision neural network models, we preserve the train-time parameter
count, thus out-performing XNOR-Nets without a train-time memory penalty. Such
training routines could see great utility in semi-supervised online learning
tasks. Our method also offers advantages in model compression, as we reduce the
model size of ResNet-18 by 7.43 times with respect to a full precision model
without utilizing any other compression techniques. We also demonstrate a
'Hadamard Binary Matrix Multiply' kernel, which delivers a 10-fold increase in
performance over full precision matrix multiplication with a similarly
optimized kernel.
