Memristive Electronics for Neuromorphic Engineering and Computing

用于神经形态工程和计算的忆阻电子学

• 批准号: 2094654
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2094654
• 关键词: Memristive; Electronics; Neuromorphic; Engineering; Computing

The objective of the work is to exploit the use of memristive/resistive RAM (RRAM) devices for novel neuromorphic (neuro-inspired) computing architectures and systems. Such systems have massive potential in delivering solutions that are far superior to existing CMOS hardware in implementation of machine learning (ML) and machine intelligence. One of the main benefits is a significant reduction in circuit complexity and vast improvements in power efficiency. This is crucial for bringing ML in adaptive embedded systems. Furthermore, this could be leveraged in "Internet/Intelligence of things" era with ML algorithms implemented directly on board, facilitating efficient local data processing and enabling devices to make decisions locally, rather than to rely on data streaming and latency-prone cloud computing.Resistive RAM (RRAM) technology, a subclass of memristive systems, is based on simple two terminal nanodevices that can repeatedly vary their resistance, with low operational energy and very high levels of integration. Remarkably, they resemble different neuronal functions - most importantly a synaptic-like plasticity by gradually changing their resistance ("synaptic weights"). By utilising the "computing-in-memory", it is possible to solve the long-lasting problem of the "von Neumann bottleneck": the need to continually shuffle data between processing cores and memory. Furthermore, implementation of hardware neural networks can be a key enabling factor for high-density, low-power neuromorphic systems.The project will be the mix of modelling and experimental work and will include the following components/objectives:Fabrication and structural characterisation of oxide-based neuromorphic devices;Demonstration and optimisation of devices' intrinsic adaptive ("synaptic-like") capabilities;Simulation and building neuromorphic circuit systems;During the PhD, the student will gain an extensive knowledge of semiconductor processing and characterisation techniques, novel semiconductor physics, neuromorphic architectures and neuromorphic computing algorithms.The project is relevant for the Artificial Intelligence theme within ICT.

这项工作的目的是利用记忆/电阻RAM （RRAM）设备用于新型神经形态（神经启发）计算架构和系统。这样的系统在提供解决方案方面具有巨大的潜力，在实现机器学习（ML）和机器智能方面远远优于现有的CMOS硬件。其中一个主要的好处是电路复杂性的显著降低和功率效率的巨大提高。这对于将ML引入自适应嵌入式系统至关重要。此外，这可以在“互联网/物联网”时代利用机器学习算法直接在船上实施，促进高效的本地数据处理，使设备能够在本地做出决策，而不是依赖数据流和易于延迟的云计算。电阻式RAM （RRAM）技术是忆阻系统的一个子类，它基于简单的两个终端纳米器件，可以反复改变其电阻，具有低操作能量和非常高的集成度。值得注意的是，它们类似于不同的神经元功能——最重要的是，通过逐渐改变它们的阻力（“突触权重”），它们具有类似突触的可塑性。通过利用“内存计算”，有可能解决长期存在的“冯·诺伊曼瓶颈”问题：需要在处理核心和内存之间不断地打乱数据。此外，硬件神经网络的实现是实现高密度、低功耗神经形态系统的关键因素。该项目将是建模和实验工作的结合，将包括以下组成部分/目标：基于氧化物的神经形态装置的制造和结构表征；演示和优化设备的内在自适应（“类突触”）能力；模拟和构建神经形态电路系统；在博士期间，学生将获得半导体处理和表征技术，新型半导体物理，神经形态架构和神经形态计算算法的广泛知识。该项目与ICT中的人工智能主题相关。

项目成果

badcrossbar: A Python tool for computing and plotting currents and voltages in passive crossbar arrays

badcrossbar：一个用于计算和绘制无源交叉阵列中电流和电压的 Python 工具

• DOI: 10.1016/j.softx.2020.100617
• 发表时间: 2020
• 期刊: SoftwareX
• 影响因子: 3.4
• 作者: Joksas D

Nonideality-Aware Training for Accurate and Robust Low-Power Memristive Neural Networks.

• DOI: 10.1002/advs.202105784
• 发表时间: 2022-06
• 期刊: ADVANCED SCIENCE
• 影响因子: 15.1
• 作者: Joksas, Dovydas;Wang, Erwei;Barmpatsalos, Nikolaos;Ng, Wing H.;Kenyon, Anthony J.;Constantinides, George A.;Mehonic, Adnan
• 通讯作者: Mehonic, Adnan

Memristive, Spintronic, and 2D-Materials-Based Devices to Improve and Complement Computing Hardware

• DOI: 10.1002/aisy.202200068
• 发表时间: 2022-07-01
• 期刊: ADVANCED INTELLIGENT SYSTEMS
• 影响因子: 7.4
• 作者: Joksas, Dovydas;AlMutairi, AbdulAziz;Mehonic, Adnan
• 通讯作者: Mehonic, Adnan