Memristor-based Architectures for Neuromorphic Computing

提出的研究目标是：(a)进一步发展基于记忆的体系结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、计算机算法等。这需要开发各种工具，如细胞库；使用不同技术制造的忆阻器的精确建模和更好的忆阻器交叉栅阵列架构。忆阻神经网络存在器件不均匀性、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程序化电导状态的统计变化和波动，以及对电脉冲缺乏线性和对称响应。我们的目的是研究这些问题，并为其中一些问题找到解决办法。我们还致力于通过减少该架构所需的晶体管来开发面积高效的镜像记忆栅架构。在该器件的其他应用中，我们将研究基于忆阻器的峰值神经网络实现。这种新兴器件已被认为是纳米电子电路的可行技术。这一领域的研究对于加拿大在世界上保持竞争力并保持其领先地位非常重要。(b)尖峰神经网络（SNNs）最近出现了一个很好的潜力，广泛的应用，如模式识别，聚类等，因为它被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，即；生物学上可信的模型，生物学上受启发的模型和高级模型。对于实际实现来说，这些模型大多非常复杂。我们的目标是为snn的数字实现开发准确有效的模型，该模型与文献中提出的模型非常接近。我们将权衡资源需求和更高的准确性，使设计人员能够选择适合他/她的应用程序的模型。我们也在寻找通过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，对基于忆阻器的SNN的设计及其学习进行研究。这项研究取决于开发具有竞争力的低成本、低功耗、高速和区域高效的数字信号处理算法和架构，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。

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.