Related papers: Antiferromagnetic Skyrmion based Energy-Efficient …
Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of…
Hardware implementation of neuromorphic computing can significantly improve performance and energy efficiency of machine learning tasks implemented with spiking neural networks (SNNs), making these hardware platforms particularly suitable…
The unique features of ultrafast spin dynamics and the absence of macroscopic magnetization in antiferromagnetic (AFM) materials provide a distinct route towards high-speed magnetic storage devices with low energy consumption and high…
Spiking Neural Networks (SNNs) offer promising energy efficiency advantages, particularly when processing sparse spike trains. However, their incompatibility with traditional datasets, which consist of batches of input vectors rather than…
Searching for low-power-consuming and high-efficient methods for well controllable driving of skyrmion motion is one of the most concerned issues for future spintronic applications, raising high concern with an appreciated choice of…
Neuromorphic models take inspiration from the human brain by adopting bio-plausible neuron models to build alternatives to traditional Machine Learning (ML) and Deep Learning (DL) solutions. The scarce availability of dedicated hardware…
Skyrmionic devices exhibit energy-efficient and high-integration data storage and computing capabilities due to their small size, topological protection, and low drive current requirements. So, to realize these devices, an extensive study,…
We present a design-scheme for ultra-low power neuromorphic hardware using emerging spin-devices. We propose device models for 'neuron', based on lateral spin valves and domain wall magnets that can operate at ultra-low terminal voltage of…
Spintronics uses spins, the intrinsic angular momentum of electrons, as an alternative for the electron charge. Its long-term goal is in the development of beyond-Moore low dissipation technology devices. Recent progress demonstrated the…
The dynamics of a spin torque driven ferrimagnetic (FiM) system is investigated using the two-sublattice macrospin model. We demonstrate an ultrafast switching in the picosecond range. However, we find that the excessive current leads to…
Eye tracking for wearable systems demands low latency and milliwatt-level power, but conventional frame-based pipelines struggle with motion blur, high compute cost, and limited temporal resolution. Such capabilities are vital for enabling…
Spiking neural networks (SNNs) with leaky integrate and fire (LIF) neurons, can be operated in an event-driven manner and have internal states to retain information over time, providing opportunities for energy-efficient neuromorphic…
An ideal layered $\hat{x}$-polarized antiferromagnet (AFM) between two antialigned $\pm \hat{z}$ polarized ferromagnetic (FM) contacts transmits no current due to a $\pi$ phase difference of the matrix elements coupling the spin degenerate…
This paper presents ASPEN, a novel energy-aware technique for neuromorphic systems that could unleash the future of intelligent, always-on, ultra-low-power, and low-burden wearables. Our main research objectives are to explore the…
This paper presents a comprehensive evaluation of Spiking Neural Network (SNN) neuron models for hardware acceleration by comparing event driven and clock-driven implementations. We begin our investigation in software, rapidly prototyping…
Antiferromagnets (AFMs) are widely believed to be superior than ferromagnets in spintronics because of their high stability due to the vanishingly small stray field. It is thus expected that the order parameter of AFM should always align…
This paper explores the energy landscape of ferromagnetic multilayer heterostructures that feature magnetic skyrmions -- tiny whirls of spins with non-trivial topology -- in each magnetic layer. Such magnetic heterostructures have been…
Materials hosting magnetic skyrmions at room temperature could enable new computing architectures as well as compact and energetically efficient magnetic storage such as racetrack memories. In a racetrack device, information is coded by the…
The spiking neural network (SNN) using leaky-integrated-and-fire (LIF) neurons has been commonly used in automatic speech recognition (ASR) tasks. However, the LIF neuron is still relatively simple compared to that in the biological brain.…
In recent years, the field of antiferromagnetic spintronics has been substantially advanced. Electric-field control is a promising approach to achieving ultra-low power spintronic devices via suppressing Joule heating. In this article,…