Using Programmable Graphene Channels as Weights in Spin-Diffusive Neuromorphic Computing

Spin channels in functionalized graphene nanoribbons.

We characterize the transport properties of functionalized graphene nanoribbons using extensive first-principles calculations based on density functional theory (DFT) that encompass both monovalent and divalent ligands, hydrogenated defects, and vacancies. We find that the edge metallic states are preserved under a variety of chemical environments, while bulk conducting channels can be easily d...

Proposal For Neuromorphic Hardware Using Spin Devices

We present a design-scheme for ultra-low power neuromorphic hardware using emerging spindevices. We propose device models for ‘neuron’, based on lateral spin valves and domain wall magnets that can operate at ultra-low terminal voltage of ~20 mV, resulting in small computation energy. Magnetic tunnel junctions are employed for interfacing the spin-neurons with charge-based devices like CMOS, fo...

Tutorial: Neuromorphic computing

Computing OWA weights as relevance factors

* On leave of absence from the Department of Electronics and Computers, Transylvania University of Braşov. Abstract – Ordered Weighted Aggregation (OWA) operators represent a distinct family of aggregation operators and were introduced by Yager in [1]. They compute a weighted sum of a number of criteria that must be satisfied. The central element of the OWA operators is that the criteria are re...

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...