Related papers: Spin-Orbit Torque Induced Spike-Timing Dependent P…
Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking…
Real-time biosignal processing on wearable devices has attracted worldwide attention for its potential in healthcare applications. However, the requirement of low-area, low-power and high adaptability to different patients challenge…
Spin-orbit coupling in inversion-asymmetric magnetic crystals and structures has emerged as a powerful tool to generate complex magnetic textures, interconvert charge and spin under applied current, and control magnetization dynamics.…
In this paper we discuss the potential of emerging spintorque devices for computing applications. Recent proposals for spinbased computing schemes may be differentiated as all-spin vs. hybrid, programmable vs. fixed, and, Boolean vs.…
The emergence of nano-scale memristive devices encouraged many different research areas to exploit their use in multiple applications. One of the proposed applications was to implement synaptic connections in bio-inspired neuromorphic…
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…
Neuromorphic computing, which seeks to replicate the brain's ability to process information, has garnered significant attention due to its potential to achieve brain-like computing efficiency and human cognitive intelligence. Spin-orbit…
Spin-torque memristors were proposed in 2009, which could provide fast, low-power and infinite memristive behavior for large-density non-volatile memory and neuromorphic computing. However, the strict requirements of combining high…
Voltage-driven spin transfer torques in magnetic tunnel junctions provide an outstanding tool to design advanced spin-based devices for memory and reprogrammable logic applications. The non-linear voltage dependence of the torque has a…
Nanoscale metal oxide memristors have potential in the development of brain-inspired computing systems that are scalable and efficient1-3. In such systems, memristors represent the native electronic analogues of the biological synapses.…
Neuromorphic computing, inspired by the brain's parallel and energy-efficient processing, offers a transformative approach to artificial intelligence. In this study, we fabricated optimized spin-transfer torque nano-oscillators (STNOs) and…
Future neuromorphic architectures will require millions of artificial synapses, making understanding the physical mechanisms behind their plasticity functionalities mandatory. In this work, we propose a simplified spin memristor, where the…
Electrophysiological techniques have improved substantially over the past years to the point that neuroprosthetics applications are becoming viable. This evolution has been fuelled by the advancement of implantable microelectrode…
We consider a fully-connected network of leaky integrate-and-fire neurons with spike-timing-dependent plasticity. The plasticity is controlled by a parameter representing the expected weight of a synapse between neurons that are firing…
Several learning rules for synaptic plasticity, that depend on either spike timing or internal state variables, have been proposed in the past imparting varying computational capabilities to Spiking Neural Networks. Due to design…
We present a simple and fast method to simulate spin-torque driven magnetisation dynamics in nano-pillar spin-valve structures. The approach is based on the coupling between a spin transport code based on random matrix theory and a…
Bio-inspired hardware holds the promise of low-energy, intelligent and highly adaptable computing systems. Applications span from automatic classification for big data management, through unmanned vehicle control, to control for bio-medical…
Bayesian inference provides a principled framework for understanding brain function, while neural activity in the brain is inherently spike-based. This paper bridges these two perspectives by designing spiking neural networks that simulate…
The ability to switch magnetic elements by spin-orbit-induced torques has recently attracted much attention for a path towards high-performance, non-volatile memories with low power consumption. Realizing efficient spin-orbit-based…
Fabricating powerful neuromorphic chips the size of a thumb requires miniaturizing their basic units: synapses and neurons. The challenge for neurons is to scale them down to submicrometer diameters while maintaining the properties that…