An Information Theory Inspired Study of Memristor Devices and their Potential Use in Neuromorphic Circuits

信息论启发忆阻器器件及其在神经形态电路中的潜在用途的研究

• 批准号: 2283690
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2283690
• 关键词: Information; Theory; Inspired; Study; Memristor

This proposed project is aligned with the EPSRC Engineering theme and is best categorised under the Artificial Intelligence Technologies Research Area. It aims to explore, from a communications and information-theoretic perspective, the potential use of memristor devices as synaptic circuit elements in neuromorphic circuits, inspired by Friston's Free Energy Principle to explain the function of the brain [1]. I would like to explore the potential to use devices called memristors (a class of devices whose resistance can be modulated using an applied voltage. They were formalised as a new two-terminal circuit element by Leon Chua in 1971 [2], to complete the set of 4 ideal passive circuit elements) to practically implement the processes described by the theory, through simulation and, if possible, through practical demonstration. Some forms of memristor are good candidates for mimicking the function of binary activation units with a nonlinear thresholding (such as neurons in the human brain) for use in novel neural architectures.I have previously explored the potential use of memristors as storage devices, modelling them as communication channels using a Generative Adversarial Network (GAN), and using an Autoencoder architecture to compress and transmit data over the noisy memristor channel: a technique known as Deep Joint Source-Channel Coding. Such techniques from deep learning can be extended throughout the course of the project in order to model the devices and their non-idealities, such as imperfect values after programming or resistance drift over time.The idea is to create a neuromorphic architecture that uses control and optimisation rules to learn. These rules will come from the dynamics of a physical system rather than from programmed rules and will be implemented through negative feedback of an error function in an electronic circuit. This method of optimisation requires no explicit gradient computation, in contrast to the explicit computation of the gradient of the parameters with respect to a loss function, as the overwhelming majority of current neural network architectures in the field of Machine Learning and Deep Learning perform through the algorithm of back propagation.Current memristive neural networks have attempted to translate the algorithm of back propagation into hardware - using Ohm's law for multiplication and Kirchhoff's current law for addition. They do however demonstrate another advantage: a reduction in power consumption and increase in speed. Many machine learning algorithms run on GPU hardware, using an external memory. For memristive neurons, processing and storage (of weights of the network) are separate from one another. Processing and memory are associated due to a fundamental shift in architecture: memristors have their own storage in the form of the resistance value that they retain after they have been controlled by a voltage or a current. This means that processing and storage are both done in a so called "in-situ" fashion - both in the same location. This is a move from the traditional Von-Neumann (separate storage and processor) architecture of computers towards architectures that function more similarly to the biological, spiking networks found in the human brain. This reduces power and time expended in transferring data between a processing unit and a storage device.[1] Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, Vol. 11, pp. 127-138.[2] L. Chua, "Memristor-The missing circuit element," IEEE Trans. Circuit Theory, vol. 18, no. 5, pp. 507-519, 1971.

这个拟议的项目与EPSRC工程主题保持一致，最好归入人工智能技术研究领域。它的目的是从通信和信息理论的角度，探索在神经形态电路中将忆阻器装置作为突触电路元件的潜在用途，灵感来自弗里斯顿的自由能原理，以解释大脑的功能[1]。我想探索一下使用被称为忆阻器的装置的可能性(一类其电阻可以通过施加的电压来调节的装置)。它们是由Leon Chua在1971年形成的一种新的双端电路元件[2]，以完成4个理想无源电路元件的集合)，以实际实现理论所描述的工艺，通过模拟，如果可能，通过实际演示。某些形式的忆阻器可以很好地模拟具有非线性阈值的二进制激活单元的功能(例如人脑中的神经元)，用于新的神经体系结构。我以前曾探索过将忆阻器用作存储设备的潜在用途，使用生成性对抗网络(GAN)将其建模为通信通道，并使用自动编码器体系结构在噪声较大的忆阻器通道上压缩和传输数据：一种称为深度联合信源-通道编码的技术。这种来自深度学习的技术可以在整个项目过程中扩展，以便对设备及其非理想状态进行建模，例如编程后不完美的值或随着时间的推移电阻漂移。其想法是创建一种使用控制和优化规则学习的神经形态体系结构。这些规则将来自物理系统的动态而不是来自编程规则，并且将通过电子电路中误差函数的负反馈来实现。这种优化方法不需要显式的梯度计算，而不是显式地计算参数相对于损失函数的梯度，因为当前机器学习和深度学习领域的绝大多数神经网络结构通过反向传播算法来执行。当前的记忆神经网络试图将反向传播算法转化为硬件-使用欧姆定律进行乘法，使用基尔霍夫电流定律进行加法。然而，它们确实展示了另一个优势：降低了功耗，提高了速度。许多机器学习算法运行在使用外部存储器的GPU硬件上。对于记忆神经元，(网络权重的)处理和存储是彼此分开的。处理和记忆是由于体系结构的根本转变而联系在一起的：忆阻器有自己的存储，其形式为电阻值，在它们被电压或电流控制后保持不变。这意味着处理和存储都是以所谓的“就地”方式完成的--两者都在同一地点。这是从传统的冯-诺伊曼(独立存储和处理器)计算机体系结构向功能更类似于人类大脑中的生物尖峰网络的体系结构的转变。这减少了在处理单元和存储设备之间传输数据所花费的功率和时间。自由能原理：统一的大脑理论？《自然评论神经科学》，第11卷，第127-138页。[2]L.Chua，《忆阻器--缺失的电路元件》，IEEE摘编。《电路理论》，第18卷，第5期，第507-519页，1971年。