Related papers: Spin-Based Neuron Model with Domain Wall Magnets a…
In recent years the field of neuromorphic low-power systems that consume orders of magnitude less power gained significant momentum. However, their wider use is still hindered by the lack of algorithms that can harness the strengths of such…
Traditional protection systems for microgrids, which rely on high fault currents and continuous communication, struggle to keep up with the changing dynamics and cybersecurity concerns of decentralized networks. In this study, we introduce…
Embedded systems acquire information about the real world from sensors and process it to make decisions and/or for transmission. In some situations, the relationship between the data and the decision is complex and/or the amount of data to…
Biological neurons and their in-silico emulations for neuromorphic artificial intelligence (AI) use extraordinarily energy-efficient mechanisms, such as spike-based communication and local synaptic plasticity. It remains unclear whether…
This paper introduces a new, highly energy-efficient, Adiabatic Capacitive Neuron (ACN) hardware implementation of an Artificial Neuron (AN) with improved functionality, accuracy, robustness and scalability over previous work. The paper…
Human brain is functionally and physically complex. This 'complexity' can be seen as a result of biological design process involving extensive use of concepts such as modularity and hierarchy. Over the past decade, deeper insights into the…
A graphene-based spin-diffusive (GrSD) neural network is presented in this work that takes advantage of the locally tunable spin transport of graphene and the non-volatility of nanomagnets. By using electrostatically gated graphene as…
Spiking artificial neurons emulate the voltage spikes of biological neurons, and constitute the building blocks of a new class of energy efficient, neuromorphic computing systems. Antiferromagnetic materials can, in theory, be used to…
A novel high-fan-in differential superconductor neuron structure designed for ultra-high-performance Spiking Neural Network (SNN) accelerators is presented. Utilizing a high-fan-in neuron structure allows us to design SNN accelerators with…
Nature inspired neuromorphic architectures are being explored as an alternative to imminent limitations of conventional complementary metal-oxide semiconductor (CMOS) architectures. Utilization of such architectures for practical…
Networks of superconducting optoelectronic neurons are investigated for their near-term technological potential and long-term physical limitations. Networks with short average path length, high clustering coefficient, and power-law degree…
A diode, a device allowing unidirectional signal transmission, is a fundamental element of logic structures and lies in the heart of modern information systems. Spin wave or magnon, representing a collective quasi-particle excitation of the…
We theoretically investigate the transfer of angular momentum between a spin superfluid and a domain wall in an exchange coupled easy-axis and easy-plane magnetic insulator system. A domain wall in the easy-axis magnet absorbs spin angular…
Since the experimental discovery of magnetic skyrmions achieved one decade ago, there have been significant efforts to bring the virtual particles into all-electrical fully functional devices, inspired by their fascinating physical and…
Artificial modulation of a neuronal subset through ion channels activation can initiate firing patterns of an entire neural circuit in vivo. As nanovalves in the cell membrane, voltage-gated ion channels can be artificially controlled by…
Neuromorphic computing which aims to mimic the collective and emergent behavior of the brain's neurons, synapses, axons, dendrites offers an intriguing, potentially disruptive solution to society's ever-growing computational needs. Although…
The rapid growth of brain-inspired computing coupled with the inefficiencies in the CMOS implementations of neuromrphic systems has led to intense exploration of efficient hardware implementations of the functional units of the brain,…
We present an account of neuroplasticity with respect to cell-internal processing pathways in relation to membrane and synaptic plasticity. We think traditional synapse-centric, weight-based models of memorization are not sufficient or…
Recent artificial neural network architectures improve performance and power dissipation by leveraging resistive devices to store and multiply synaptic weights with input data. Negative and positive synaptic weights are stored on the…
Energy-efficiency and design-complexity of high-speed on-chip and inter-chip data-interconnects has emerged as the major bottleneck for high-performance computing-systems. As a solution, we propose an ultra-low energy interconnect…