Ferroelectric materials for neuromorphic computing

Tutorial: Neuromorphic computing

ReRAM-Based Neuromorphic Computing

Artificial neural networks are currently being used as a promising means to solve problems in machine learning and computer science [1–3]. These networks are loosely inspired by massively parallel biological neural systems, but they are often implemented on mostly sequential hardware computing platforms, ignoring key features of real neural processing systems, such as their ability to carry out...

Automotive Computing, Neuromorphic Computing, and Beyond

Welcome to 2018 and IEEE Micro’s 38th year. I’m delighted about last year’s issues. In addition to the traditional special issues on Hot Chips and Top Picks, we had special issues on cognitive architectures, architectures for the post-Moore era, Cool Chips and Hot Interconnects, and ultra-low-power processors. I deeply appreciate the efforts of everyone who reviewed the many articles submitted ...

Backpropagation for Energy-Efficient Neuromorphic Computing

Solving real world problems with embedded neural networks requires both training algorithms that achieve high performance and compatible hardware that runs in real time while remaining energy efficient. For the former, deep learning using backpropagation has recently achieved a string of successes across many domains and datasets. For the latter, neuromorphic chips that run spiking neural netwo...

Novel synaptic memory device for neuromorphic computing

This report discusses the electrical characteristics of two-terminal synaptic memory devices capable of demonstrating an analog change in conductance in response to the varying amplitude and pulse-width of the applied signal. The devices are based on Mn doped HfO₂ material. The mechanism behind reconfiguration was studied and a unified model is presented to explain the underlying device physics...