Subthreshold neuromorphic devices for Spiking Neural Networks applied to embedded A.I

Energy autonomy is one of the major challenges of embedded Artificial Intelligence. Among the candidate technologies likely to take up such a challenge, spiking neural networks are the most promising because of both their spatio-temporal and sparse representation of the information. In this context, this paper presents a neuromorphic approach based on an industrial CMOS technology and adopting an ...