  We introduce Nerva, a fast neural network library under development in C++.
It supports sparsity by using the sparse matrix operations of Intel's Math
Kernel Library (MKL), which eliminates the need for binary masks. We show that
Nerva significantly decreases training time and memory usage while reaching
equivalent accuracy to PyTorch. We run static sparse experiments with an MLP on
CIFAR-10. On high sparsity levels like $99\%$, the runtime is reduced by a
factor of $4\times$ compared to a PyTorch model using masks. Similar to other
popular frameworks such as PyTorch and Keras, Nerva offers a Python interface
for users to work with.
