Parallel in-memory wireless computing

TL;DR: The upcoming BNIs are profiled by introducing the brief history of neuromorphics, reviewing the recent progresses on related areas, and discussing the future advances and challenges that lie ahead.

TL;DR: Researchers develop a bio-inspired crossmodal in-sensor computing system using flexible VO2 memristors, enabling real-time energy-efficient processing of multimodal signals for wearable human-machine interfaces with high accuracy and flexibility.

TL;DR: IGZO/WO3-x heterostructured artificial optoelectronic synaptic devices mimic image segmentation and motion capture, exhibiting high-performance optoelectronic synaptic responses and repeatable linear synaptic weight changes.

TL;DR: The fabrication of high-yield, high-performance and uniform memristor crossbar arrays for the implementation of CNNs and an effective hybrid-training method to adapt to device imperfections and improve the overall system performance are proposed.

TL;DR: This Review Article examines the development of in-memory computing using resistive switching devices, where the two-terminal structure of the devices, theirresistive switching properties, and direct data processing in the memory can enable area- and energy-efficient computation.

TL;DR: The state of the art in memristor-based electronics is evaluated and the future development of such devices in on-chip memory, biologically inspired computing and general-purpose in-memory computing is explored.

TL;DR: The challenges in the integration and use in computation of large-scale memristive neural networks are discussed, both as accelerators for deep learning and as building blocks for spiking neural networks.