Quantum 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 ...

Neuromorphic Adiabatic Quantum Computation

Real parallel computing is possible with a quantum computer which makes use of quantum states (Nielsen & Chuang, 2000). It is well known that a quantum computation algorithm for factorization discovered by Shor (1994) is faster than any classical algorithm. Also a quantum algorithm for database search in ( ) O N quantum computation steps instead of O(N) classical steps has been proposed by Grov...

Large-scale neuromorphic computing systems.

Neuromorphic computing covers a diverse range of approaches to information processing all of which demonstrate some degree of neurobiological inspiration that differentiates them from mainstream conventional computing systems. The philosophy behind neuromorphic computing has its origins in the seminal work carried out by Carver Mead at Caltech in the late 1980s. This early work influenced other...