  Artificial neural networks are notoriously power- and time-consuming when
implemented on conventional von Neumann computing systems. Consequently, recent
years have seen an emergence of research in machine learning hardware that
strives to bring memory and computing closer together. A popular approach is to
realise artificial neural networks in hardware by implementing their synaptic
weights using memristive devices. However, various device- and system-level
non-idealities usually prevent these physical implementations from achieving
high inference accuracy. We suggest applying a well-known concept in computer
science -- committee machines -- in the context of memristor-based neural
networks. Using simulations and experimental data from three different types of
memristive devices, we show that committee machines employing ensemble
averaging can successfully increase inference accuracy in physically
implemented neural networks that suffer from faulty devices, device-to-device
variability, random telegraph noise and line resistance. Importantly, we
demonstrate that the accuracy can be improved even without increasing the total
number of memristors.
