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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=741336
• 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.

拟议研究的目标是： (a) 进一步开发最先进的基于忆阻的架构和电路，用于神经形态计算、有限域乘法器、数字滤波器、计算机算术等各种应用。这需要开发细胞库等各种工具；使用不同技术和更好的忆阻器交叉阵列架构制造的忆阻器的精确建模。忆阻神经网络存在器件不均匀、电阻水平不稳定、潜行路径电流和导线电阻等问题。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，以及缺乏对电脉冲的线性和对称响应。我们的目标是研究这些问题并找到一些解决方案。我们还旨在通过减少架构中所需的晶体管来开发面积高效的镜像忆阻交叉架构。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。这种新兴设备被认为是纳米电子电路的可行技术。该领域的研究对于加拿大保持世界竞争力并保持领先地位非常重要。 (b) 尖峰神经网络（SNN）最近在模式识别、聚类等广泛应用中具有良好的潜力，因为事实证明这些网络与大脑的活动和功能非常相似。文献中报道了许多模型来精确定义它们的行为和神经元的功能。这些模型分为三类：生物学合理模型、生物学启发模型和高级模型。这些模型对于实际实施来说大多非常复杂。我们的目标是为 SNN 的数字实现开发准确有效的模型，该模型非常接近文献中提出的模型。我们将考虑资源需求与更高准确度之间的权衡，使设计人员能够选择适合其应用的模型。我们还在研究通过尖峰时间相关可塑性 (STDP) 训练这些 SNN 的新方法，以用于模式识别等应用。最终，将研究基于忆阻器的SNN的设计及其学习。这项研究取决于开发具有竞争力的低成本、低功耗、高速和面积高效的数字信号处理算法和架构，这些算法和架构能够实现协同作用，并有可能增强加拿大在智能安全系统领域的竞争力。