Related papers: Using Programmable Graphene Channels as Weights in…
We demonstrate with a fully quantum-mechanical approach that graphene can function as gate-controllable transistors for pumped spin currents, i.e., a stream of angular momentum induced by the precession of adjacent magnetizations, which…
This paper proposes a distributed learning-based framework to tackle the sum ergodic rate maximization problem in cell-free massive multiple-input multiple-output (MIMO) systems by utilizing the graph neural network (GNN). Different from…
As a powerful tool for modeling graph data, Graph Neural Networks (GNNs) have received increasing attention in both academia and industry. Nevertheless, it is notoriously difficult to deploy GNNs on industrial scale graphs, due to their…
We demonstrate the design of a neural network, where all neuromorphic computing functions, including signal routing and nonlinear activation are performed by spin-wave propagation and interference. Weights and interconnections of the…
Integrating magnetic skyrmions into neuromorphic computing could help improve hardware efficiency and computational power. However, developing a scalable implementation of the weighted sum of neuron signals - a core operation in neural…
Neuromorphic devices have gained significant attention as potential building blocks for the next generation of computing technologies owing to their ability to emulate the functionalities of biological nervous systems. The essential…
Graph Neural Networks (GNNs) show strong promise for circuit analysis, but scaling to modern large-scale circuit graphs is limited by GPU memory and training cost, especially for deep models. We revisit deep GNNs for circuit graphs and show…
We present distributed algorithms for training dynamic Graph Neural Networks (GNN) on large scale graphs spanning multi-node, multi-GPU systems. To the best of our knowledge, this is the first scaling study on dynamic GNN. We devise…
This study analyzes Graph Neural Networks (GNNs) for distribution system state estimation (DSSE) by employing an interpretable Graph Neural Additive Network (GNAN) and by utilizing an edge-conditioned message-passing mechanism. The…
Simulation of spiking neural networks has been traditionally done on high-performance supercomputers or large-scale clusters. Utilizing the parallel nature of neural network computation algorithms, GeNN (GPU Enhanced Neural Network)…
Spin dependent electron transport measurements on graphene are of high importance to explore possible spintronic applications. Up to date all spin transport experiments on graphene were done in a semi-classical regime, disregarding quantum…
Selectively programming large number of non-volatile synaptic weights without compromising scalability is a key challenge for in-memory computing. Here, we demonstrate remote programming of synaptic weights in series-connected chains of 11…
We propose encapsulating type-A antiferromagnetic semiconductors between graphene layers to realize a gate-tunable synthetic antiferromagnet with nonrelativistic spin splitting, enabling efficient spintronic transport via graphene. Ab…
Graphene is a promising platform for spin-based non-volatile memory, logic, and neuromorphic computing by combining long-distance spin transport with electrical tunability at room temperature. However, advancing beyond passive spin channels…
Graphene spintronics offers a promising route to achieve low power 2D electronics for next generation classical and quantum computation. As device length scales are reduced to the limit of the electron mean free path, the transport…
Graphene nanoribbons (GNRs) have been proposed as potential building blocks for field effect transistor (FET) devices due to their quantum confinement bandgap. Here, we propose a novel GNR device concept, enabling the control of both charge…
Training Graph Neural Networks (GNNs) on large graphs presents unique challenges due to the large memory and computing requirements. Distributed GNN training, where the graph is partitioned across multiple machines, is a common approach to…
The scientific community has witnessed an exponential increase in the applications of graphene and graphene-based materials in a wide range of fields. For what concerns neuroscience, the interest raised by these materials is two-fold. On…
Within the field of spintronics major efforts are directed towards developing applications for spin-based transport devices made fully out of two-dimensional (2D) materials. In this work we present an experimental realization of a…
Discrete dislocation dynamics (DDD) is a widely employed computational method to study plasticity at the mesoscale that connects the motion of dislocation lines to the macroscopic response of crystalline materials. However, the…