Mirrored Memristor Crossbar Array for Digital and Analog Implementation of Computer Arithmetic and, Spiking Neural Networks

用于计算机算术和尖峰神经网络的数字和模拟实现的镜像忆阻器交叉阵列

• 批准号: RGPIN-2019-04693
• 负责人: Ahmadi, Majid
• 金额: $ 2.04万
• 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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=682573
• 关键词: Mirrored; Memristor; Crossbar; Array; Digital

Memristor has the potential to enhance or augment several areas of integrated circuit design, computing and application. Recent progress in the design and fabrication of Memristor devices will enable them to be a core enabling technology for future affordable integrated circuits that need to be scalable, energy-efficient and reconfigurable. A Memristor's compatibility with CMOS structures make them an ideal device for various diverse applications. These include: logic operations, neuromorphic devices for high speed one and two-dimensional digital signal processing (DSP) with low power consumption, non-volatile memory, analogue electronics, and reconfigurable circuits and hardware that can learn and adapt autonomously. Memristor crossbar architectures are considered as one of the most promising platforms for future memory, logic and in-memory calculation applications.***The objectives of the proposed research are :***(a) To further develop the state-of-the-art Memristive based architectures and circuits for applications on digital design, finite field multipliers, computer arithmetic, neural networks, digital filters, few to mention. To further explore the design of arithmetic circuits using Continuous Valued Number System (CVNS) developed by the applicant and his students using Memristors for the design of a fully analogue neural network. We plan to develop an area-efficient Mirrored Memristive Crossbar Architecture by either eliminating feedback and/or reducing the number of transistors required.***(b) Spiking Neural Networks (SNNs) have emerged recently with a good potential for a wide ranges of applications like pattern recognition, clustering etc. It has been demonstrated that these networks can closely resemble some of the activities and function of the brain. There are many models reported in the literature that precisely define behavior and the functionality of their neurons. These models are mostly very complicated for practical implementation. We shall work to develop an accurate and efficient model for the digital implementation of SNNs which closely approximate models presented in the literature. We would look at the trade off for resource requirements versus higher accuracy that enable the designer to choose the model that fit his/her application. We shall also investigate novel ways of training SNNs through Spike Time Dependent Plasticity (STDP) methods for applications, such as, pattern recognition. We shall try to develop a Memrisor based digital implementation of SNNs for pattern recognition applications. The Memristor is being regarded as the viable technology for nano electronic circuits and a future replacement for many CMOS devices. Research in this area is very important for Canada to be competitive in the evolving world economy. Furthermore, this research will lead to competitive low-cost, low-power, high-speed and area-efficient computer arithmetic, and DSP algorithms and reconfigurable architectures that enable synergy.

忆阻器具有增强或增强集成电路设计、计算和应用的多个领域的潜力。忆阻器器件设计和制造的最新进展将使它们成为未来可扩展、节能和可重构的经济型集成电路的核心支持技术。忆阻器与 CMOS 结构的兼容性使其成为各种不同应用的理想器件。其中包括：逻辑运算、低功耗高速一维和二维数字信号处理 (DSP) 的神经形态设备、非易失性存储器、模拟电子器件以及可自主学习和适应的可重构电路和硬件。忆阻器交叉架构被认为是未来存储器、逻辑和内存计算应用最有前途的平台之一。***拟议研究的目标是：***(a) 进一步开发最新技术-基于忆阻艺术的架构和电路，适用于数字设计、有限域乘法器、计算机算术、神经网络、数字滤波器等应用。进一步探索使用申请人及其学生开发的连续值数系统（CVNS）的算术电路设计，使用忆阻器来设计全模拟神经网络。我们计划通过消除反馈和/或减少所需晶体管的数量来开发面积高效的镜像忆阻交叉架构。***(b) 尖峰神经网络 (SNN) 最近出现，具有广泛的应用潜力。模式识别、聚类等应用。已经证明，这些网络可以非常类似于大脑的一些活动和功能。文献中报道了许多精确定义神经元行为和功能的模型。这些模型对于实际实施来说大多非常复杂。我们将努力开发一个准确有效的模型来实现 SNN 的数字化，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。我们还将研究通过尖峰时间相关可塑性 (STDP) 方法训练 SNN 的新方法，以用于模式识别等应用。我们将尝试开发基于忆阻器的 SNN 数字实现，用于模式识别应用。忆阻器被认为是纳米电子电路的可行技术以及许多 CMOS 器件的未来替代品。该领域的研究对于加拿大在不断发展的世界经济中保持竞争力非常重要。此外，这项研究将带来具有竞争力的低成本、低功耗、高速和节省空间的计算机算法，以及能够实现协同作用的DSP算法和可重构架构。