Memristive Electronics for Neuromorphic Engineering and Computing

这项工作的目的是利用忆阻/电阻RAM（RRAM）设备的使用，用于新的神经形态（神经启发）计算架构和系统。这些系统在提供解决方案方面具有巨大的潜力，这些解决方案在实现机器学习（ML）和机器智能方面远远上级现有的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改善。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“物联网/智能”时代利用机器学习算法直接在板上实现，促进高效的本地数据处理，并使设备能够在本地做出决策，而不是依赖于数据流和易延迟的云计算。电阻式RAM（RRAM）技术是忆阻系统的一个子类，基于简单的两端纳米器件，可以重复改变它们的电阻，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电阻（“突触权重”）的突触样可塑性。通过利用“内存计算”，可以解决长期存在的“冯·诺依曼瓶颈”问题：需要在处理核心和内存之间不断地洗牌数据。此外，硬件神经网络的实现可以是高密度，低功耗neuromorphic系统的关键使能因素。该项目将是建模和实验工作的组合，并将包括以下组成部分/目标：氧化物基神经形态器件的制造和结构表征;器件内在自适应性的演示和优化模拟和构建神经形态电路系统;在博士期间，学生将获得半导体加工和表征技术，新颖的半导体物理，神经形态架构和神经形态计算算法。该项目与ICT中的人工智能主题相关。

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.