Memristor-based Architectures for Neuromorphic Computing

用于神经形态计算的基于忆阻器的架构

• 批准号: RGPIN-2020-06613
• 负责人: Ahmadi, Majid
• 金额: $ 2.4万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752018
• 关键词: Memristor; based; Architectures; Neuromorphic; Computing

The objectives of the proposed research are: (a) To further develop the state-of-the-art for Memristive-based architectures and circuits for various applications such as neuromorphic computing, finite field multipliers, digital filters, computer arithmetic etc. This requires development of various tools such as cell library; accurate modeling of Memristors fabricated using different technologies and better architectures for Memristors Crossbar Arrays. Memristive neural networks have exhibited problems, such as, device non-uniformity, resistance level instability, sneak path currents, and wire resistance. In addition, potential learning algorithms must be able to deal with statistical variations and fluctuations in programmed conductance states and the lack of linear and symmetric responses to electric pulses. Our aim is to study these problems and find solutions for some. We also aim to develop an area efficient Mirrored Memristive Crossbars architecture by reducing the transistors required in that architecture. Among other application for this device we will investigate Memristor-based implementation of Spiking Neural Networks. This emerging device has been regarded as the viable technology for Nano-electronic circuits. Research in this area is very important for Canada to stay competitive in world and keeps its place as leader. (b) Spiking Neural Networks (SNNs) have emerged recently as a good potential for wide ranges of applications like pattern recognition, clustering etc. as it is demonstrated that these networks resemble closely with the activities and function of the brain. There are many models reported in the literature to precisely define their behavior and the functionality of their neurons. These models are divided into three categories namely; Biologically-plausible models, Biologically-inspired models, and High-level models. These models are mostly very complicated for practical implementation. We are aiming to develop accurate and efficient model for digital implementation of SNNs which closely approximate models presented in the literature. We would look at trade off for resource requirements versus higher accuracy that enable the designer to choose the model that fits his/her application. We are also looking at the novel ways of training these SNNs through Spike Time Dependent Plasticity (STDP) for applications such as pattern recognition. Ultimately, research on the design of Memristor-based SNN along with its learning will be carried out. This research is contingent upon developing competitive low-cost, low-power, high-speed and area efficient digital signal processing algorithms and architectures that enable synergy and has the potential to enhance Canada's competitiveness in the area of smart security systems.

建议研究的目的是：（a）进一步发展基于忆阻的架构和电路的最新技术，用于各种应用，如神经形态计算、有限域乘法器、数字滤波器、计算机算术等。这需要开发各种工具，如单元库;使用不同技术制造的忆阻器的准确建模和更好的忆阻器交叉阵列架构。忆阻神经网络已经表现出诸如器件非均匀性、电阻水平不稳定性、潜路径电流和导线电阻的问题。此外，潜在的学习算法必须能够处理编程电导状态的统计变化和波动以及对电脉冲的线性和对称响应的缺乏。我们的目标是研究这些问题，并找到一些解决方案。我们的目标还包括通过减少该架构中所需的晶体管来开发一种面积高效的镜像忆阻交叉开关架构。在该设备的其他应用中，我们将研究基于忆阻器的尖峰神经网络的实现。这种新兴器件被认为是纳米电子电路的可行技术。这一领域的研究对加拿大保持世界竞争力和保持其领导地位非常重要。(b)尖峰神经网络（Spiking Neural Networks，SNN）是近年来兴起的一种具有广泛应用前景的神经网络，它与大脑的活动和功能非常相似，具有很好的模式识别、聚类等应用前景。文献中报道了许多模型来精确定义它们的行为和它们的神经元的功能。这些模型分为三类，即：生物合理模型，生物启发模型和高级模型。这些模型对于实际实现来说大多非常复杂。我们的目标是开发精确和高效的模型，用于SNN的数字实现，该模型与文献中提出的模型非常接近。我们将考虑资源需求与更高精度之间的权衡，使设计师能够选择适合他/她应用的模型。我们还在研究通过Spike Time Dependent Plasticity（STDP）训练这些SNN的新方法，用于模式识别等应用。最后，本文将对基于忆阻器的SNN的设计及其学习进行沿着研究。这项研究取决于开发有竞争力的低成本，低功耗，高速和区域高效的数字信号处理算法和架构，使协同作用，并有可能提高加拿大在智能安全系统领域的竞争力。