  In-band full-duplex systems can transmit and receive information
simultaneously on the same frequency band. However, due to the strong
self-interference caused by the transmitter to its own receiver, the use of
non-linear digital self-interference cancellation is essential. In this work,
we describe a hardware architecture for a neural network-based non-linear
self-interference (SI) canceller and we compare it with our own hardware
implementation of a conventional polynomial based SI canceller. In particular,
we present implementation results for a shallow and a deep neural network SI
canceller as well as for a polynomial SI canceller. Our results show that the
deep neural network canceller achieves a hardware efficiency of up to $312.8$
Msamples/s/mm$^2$ and an energy efficiency of up to $0.9$ nJ/sample, which is
$2.1\times$ and $2\times$ better than the polynomial SI canceller,
respectively. These results show that NN-based methods applied to
communications are not only useful from a performance perspective, but can also
be a very effective means to reduce the implementation complexity.
