Related papers: 2D Spintronics for Neuromorphic Computing with Sca…
Two-dimensional (2D) materials present an exciting opportunity for devices and systems beyond the von Neumann computing architecture paradigm due to their diversity of electronic structure, physical properties, and atomically-thin, van der…
Neuromorphic computing uses brain-inspired principles to design circuits that can perform computational tasks with superior power efficiency to conventional computers. Approaches that use traditional electronic devices to create artificial…
In a data-driven economy, virtually all industries benefit from advances in information technology -- powerful computing systems are critically important for rapid technological progress. However, this progress might be at risk of slowing…
Spintronic-based brain-inspired neuromorphic computing has recently attracted significant attention due to the exceptional properties of magnetic microstructures, including nanoscale dimensions, high stability, and low energy consumption.…
Neuromorphic computing and spiking neural networks aim to leverage biological inspiration to achieve greater energy efficiency and computational power beyond traditional von Neumann architectured machines. In particular, spiking neural…
Over the last decade, artificial intelligence has found many applications areas in the society. As AI solutions have become more sophistication and the use cases grew, they highlighted the need to address performance and energy efficiency…
Neuromorphic spintronics combines two advanced fields in technology, neuromorphic computing and spintronics, to create brain-inspired, efficient computing systems that leverage the unique properties of the electron's spin. In this book…
Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for…
Recent years have witnessed growing interest in the field of brain-inspired computing based on neural-network architectures. In order to translate the related algorithmic models into powerful, yet energy-efficient cognitive-computing…
Neuromorphic Computing promises orders of magnitude improvement in energy efficiency compared to traditional von Neumann computing paradigm. The goal is to develop an adaptive, fault-tolerant, low-footprint, fast, low-energy intelligent…
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…
Machine learning has emerged as the dominant tool for implementing complex cognitive tasks that require supervised, unsupervised, and reinforcement learning. While the resulting machines have demonstrated in some cases even super-human…
Neuromorphic computing and, in particular, spiking neural networks (SNNs) have become an attractive alternative to deep neural networks for a broad range of signal processing applications, processing static and/or temporal inputs from…
Spiking neural networks (SNNs) are powerful models of spatiotemporal computation and are well suited for deployment on resource-constrained edge devices and neuromorphic hardware due to their low power consumption. Leveraging attention…
Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic…
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and…
As humans advance toward a higher level of artificial intelligence, it is always at the cost of escalating computational resource consumption, which requires developing novel solutions to meet the exponential growth of AI computing demand.…
Novel computational paradigms may provide the blueprint to help solving the time and energy limitations that we face with our modern computers, and provide solutions to complex problems more efficiently (with reduced time, power consumption…
The large variety of 2D materials and their co-integration in van der Waals (vdW) heterostructures enable innovative device engineering. In addition, their atomically-thin nature promotes the design of artificial materials by proximity…
Spintronics has become a broad and important research field that intersects with magnetism, nano-electronics, and materials science. Its overarching aim is to provide a fundamental understanding of spin-dependent phenomena in solid-state…