  RRAM-based in-Memory Computing is an exciting road for implementing highly
energy efficient neural networks. This vision is however challenged by RRAM
variability, as the efficient implementation of in-memory computing does not
allow error correction. In this work, we fabricated and tested a differential
HfO2-based memory structure and its associated sense circuitry, which are ideal
for in-memory computing. For the first time, we show that our approach achieves
the same reliability benefits as error correction, but without any CMOS
overhead. We show, also for the first time, that it can naturally implement
Binarized Deep Neural Networks, a very recent development of Artificial
Intelligence, with extreme energy efficiency, and that the system is fully
satisfactory for image recognition applications. Finally, we evidence how the
extra reliability provided by the differential memory allows programming the
devices in low voltage conditions, where they feature high endurance of
billions of cycles.
