Manuel Le Gallo

TL;DR: This Review provides an overview of memory devices and the key computational primitives enabled by these memory devices as well as their applications spanning scientific computing, signal processing, optimization, machine learning, deep learning and stochastic computing.

TL;DR: This work shows that chalcogenide-based phase-change materials can be used to create an artificial neuron in which the membrane potential is represented by the phase configuration of the nanoscale phase- change device and shows that the temporal integration of postsynaptic potentials can be achieved on a nanosecond timescale.

TL;DR: The relevant virtues and limitations of these devices are assessed, in terms of properties such as conductance dynamic range, (non)linearity and (a)symmetry of conductance response, retention, endurance, required switching power, and device variability.

TL;DR: A multi-memristive synaptic architecture with an efficient global counter-based arbitration scheme to address challenges associated with the non-ideal memristive device behavior is proposed.

TL;DR: The results indicate the potential of integrated photonics for parallel, fast, and efficient computational hardware in data-heavy AI applications such as autonomous driving, live video processing, and next-generation cloud computing services.