Skyrmionic Nanodevices for Neuromorphic Computing

该项目旨在将新兴的天空仿生和神经形态计算领域连接起来，以尝试将世界自旋电子学的新解决方案引入神经形态硬件。特别是，该项目的目标是开发现实的致密纳米器件，可以作为非易失性和节能的神经形态硬件组件，可以部署在边缘计算场景中。为了实现这一目标，我们将使用i)微磁模拟来设计器件并利用天空电子学材料的有趣特性，ii)电路模拟以研究如何在混合自旋电子学- cmos方法中连接这些器件，iii)尖峰神经网络模拟以探索它们与神经形态计算范例的集成。该项目旨在设计新颖的神经形态组件，并评估其潜力、性能和节能操作，如果嵌入到边缘计算应用的完整系统/芯片中。研究问题/目标：-研究skyrmionic设备概念，以更小的占地面积和更高的能量效率模拟突触或神经元。-调查skyrmionic互连。-研究微型纳米器件与CMOS电路接口的紧凑电路模型。-研究集成skyrmionics硬件组件的最佳峰值神经网络拓扑结构。新颖的物理科学/工程内容：该项目是跨学科的，结合了EPSRC各个研究领域的研究线索：-自旋电子学-人工智能技术-凝聚态：磁性和磁性材料-普适和无处不在的计算

The project aims to bridge the burgeoning field of skyrmionics with neuromorphic computing in order to try and bring new solutions from the world spintronics into neuromorphic hardware. In particular, the goal of the project is to develop realistic dense skyrmionic nanodevices that can serve as non-volatile and energy-efficient neuromorphic hardware components that could be deployed in edge-computing scenarios. In order to achieve this, we will use i) micromagnetic simulations to design the devices and exploit the intriguing properties of skyrmionic materials, ii) circuit simulations in order to investigate how to connect these devices in a hybric spintronics-CMOS approach and iii) spiking neural network simulations in order to explore their integration into a neuromorphic computing paradigm. The project aims to design novel neuromorphic components and to evaluate their potential, performance and energy-efficient operation if embedded in full systems/chips for edge-computing applications.Research questions / Objectives:- Investigate skyrmionic device concepts that emulate synapses or neurons with smaller footprint and higher energy-efficiency.- Investigate skyrmionic interconnects.- Investigate compact circuit models that interface skyrmionic nanodevices with CMOS circuits.- Investigate optimal spiking neural network topologies for integrating skyrmionics hardware components.Approach / Methodologies:- Computational nanophysics simulations, micromagnetics and atomistic simulations- CMOS circuit simulations- Spiking Neural Network SimulationsNovel physical sciences/engineering content:The project is interdisciplinary and combines research threads from various EPSRC research areas: - spintronics- artificial intelligence technologies- Condensed matter: magnetism and magnetic materials- Pervasive and ubiquitous computing