  As it is getting increasingly difficult to achieve gains in the density and
power efficiency of microelectronic computing devices because of lithographic
techniques reaching fundamental physical limits, new approaches are required to
maximize the benefits of distributed sensors, micro-robots or smart materials.
Biologically-inspired devices, such as artificial neural networks, can process
information with a high level of parallelism to efficiently solve difficult
problems, even when implemented using conventional microelectronic
technologies. We describe a mechanical device, which operates in a manner
similar to artificial neural networks, to solve efficiently two difficult
benchmark problems (computing the parity of a bit stream, and classifying
spoken words). The device consists in a network of masses coupled by linear
springs and attached to a substrate by non-linear springs, thus forming a
network of anharmonic oscillators. As the masses can directly couple to forces
applied on the device, this approach combines sensing and computing functions
in a single power-efficient device with compact dimensions.
