Related papers: Using Programmable Graphene Channels as Weights in…
Spintronics, which utilizes spin as information carrier, is a promising solution for nonvolatile memory and low-power computing in the post-Moore era. An important challenge is to realize long distance spin transport, together with…
Nanomagnets driven by spin currents provide a natural implementation for a neuron and a synapse: currents allow convenient summation of multiple inputs, while the magnet provides the threshold function. The objective of this paper is to…
Spintronic devices are considered as promising candidates in implementing neuromorphic systems or hardware neural networks, which are expected to perform better than other existing computing systems for certain data classification and…
Flexible metal oxide/graphene oxide hybrid multi-gate neuron transistors were fabricated on flexible graphene substrates. Dendritic integrations in both spatial and temporal modes were successfully emulated, and spatiotemporal correlated…
Neural field models represent neuronal communication on a population level via synaptic weight functions. Using voltage sensitive dye (VSD) imaging it is possible to obtain measurements of neural fields with a relatively high spatial and…
Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to digital systems. Though many devices have emerged as candidates for…
We present artificial neural network design using spin devices that achieves ultra low voltage operation, low power consumption, high speed, and high integration density. We employ spin torque switched nano-magnets for modelling neuron and…
We report the first measurements of spin injection in to graphene through a 20 nm thick tungsten disulphide (WS$_2$) layer, along with a modified spin relaxation time ({\tau}s) in graphene in the WS$_2$ environment, via spin-valve and Hanle…
Electronic carriers in graphene show a high carrier mobility at room temperature. Thus, this system is widely viewed as a potential future charge-based high-speed electronic-material to complement- or replace- silicon. At the same time, the…
Graph Visualization, also known as Graph Drawing, aims to find geometric embeddings of graphs that optimize certain criteria. Stress is a widely used metric; stress is minimized when every pair of nodes is positioned at their shortest path…
Owing to their unprecedented electronic properties, graphene and two-dimensional (2D) crystals have brought fresh opportunities for advances in planar spintronic devices. Graphene is an ideal medium for spin transport while also being an…
This paper presents a novel design concept for spintronic nanoelectronics that emphasizes a seamless integration of spin-based memory and logic circuits. The building blocks are magneto-logic gates based on a hybrid graphene/ferromagnet…
Graph neural networks (GNNs) have become powerful tools for processing graph-based information in various domains. A desirable property of GNNs is transferability, where a trained network can swap in information from a different graph…
Machine learning (ML) and deep learning (DL) techniques have gained significant attention as reduced order models (ROMs) to computationally expensive structural analysis methods, such as finite element analysis (FEA). Graph neural network…
We propose a graphene device that can generate spin-dependent negative differential resistance (NDR). The device is composed of a sufficiently wide and short graphene and two gated EuO strips deposited on top of it. This scheme avoids…
We propose to execute deep neural networks (DNNs) with dynamic and sparse graph (DSG) structure for compressive memory and accelerative execution during both training and inference. The great success of DNNs motivates the pursuing of…
Memristive neural networks (MNNs), which use memristors as neurons or synapses, have become a hot research topic recently. However, most memristors are not compatible with mainstream integrated circuit technology and their stabilities in…
Establishing a reliable communication interface between the brain and electronic devices is of paramount importance for exploiting the full potential of neural prostheses. Current microelectrode technologies for recording electrical…
We introduce Graph-Induced Sum-Product Networks (GSPNs), a new probabilistic framework for graph representation learning that can tractably answer probabilistic queries. Inspired by the computational trees induced by vertices in the context…
A spin field effect transistor (FET) is proposed by utilizing a graphene nanoribbon as the channel. Similar to the conventional spin FETs, the device involves ferromagnetic metals as a source and drain; they, in turn, are connected to the…