Collaborative Research: FET: Small: Reservoir Computing with Ion-Channel-Based Memristors

合作研究：FET：小型：基于离子通道忆阻器的储层计算

• 批准号: 2403560
• 负责人: Joseph Najem
• 金额: $ 33.67万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2403560&HistoricalAwards=false
• 关键词: Collaborative; Research; FET; Small; Reservoir

The general-purpose digital computing paradigm faces severe limitations, which could potentially be addressed by adopting more brain-inspired approaches. However, despite significant recent advancements in artificial intelligence algorithms, substantial work remains in developing hardware capable of emulating the functionality and energy efficiency of the brain. This project aims to develop novel physical reservoir computing architectures that harness the highly nonlinear dynamics of ion-channel-based memristors (ICMs). This work will develop new fabrication methods, learning algorithms, and network designs to take advantage of the unique properties of the proposed materials. This will lead towards a new paradigm for brain-inspired computing with biocompatible and highly energy-efficient hardware. The long-term goal is to develop low-cost, energy-efficient, highly tunable, modular, fault-tolerant, and self-healing biomolecular neural networks and tissues. The intended applications include signal processing, in-sensor and near-sensor computing, neuro-engineering, artificial intelligence, and post-silicon technologies. The educational impact leverages the fact that this project interfaces with topics in engineering, biology, physics, and chemistry. Students who are involved will receive exclusive scientific training, which will help prepare them for making contributions in multiple fields.Traditional reservoir computers use a reservoir layer comprising a recurrent connection of neurons with randomly assigned synaptic weights, followed by a readout layer whose nonvolatile synaptic weights are trained. The hypothesis is that both reservoir and readout layers can be combined into a single layer using ICMs that exhibit collocated volatile and non-volatile memories. The basic element in these devices is an insulating lipid membrane that mimics the composition and function of biological membranes. In the presence of voltage-activated ion channels, these synthetic lipid membranes can exhibit voltage-dependent memristance caused by membrane and ion channel dynamics. This proposed research specifically aims to: 1) understand how the specific properties of the ICMs can be harnessed for reservoir computing; 2) design architectures tailored to the nonlinear short-term memory dynamics of our devices in both the reservoir and the readout layers, and possibly combine them into a single layer; 3) experimentally validate using crossbar arrays of the ICMs; and 4) generalize the new reservoir architecture so it can be used with other nonvolatile and volatile memristors, possibly solid-state in nature.This project is jointly funded by the Software and Hardware Foundations Cluster of the Division of Computing and Communication Foundations (CCF) in the Directorate for Computer and Information Science and Engineering (CISE) and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

通用数字计算范式面临着严重的局限性，这可以通过采用更多的大脑启发方法来解决。然而，尽管人工智能算法最近取得了重大进展，但在开发能够模拟大脑功能和能源效率的硬件方面仍有大量工作要做。该项目旨在开发新的物理水库计算架构，利用离子通道为基础的忆阻器（ICM）的高度非线性动力学。这项工作将开发新的制造方法，学习算法和网络设计，以利用拟议材料的独特性能。这将导致一个新的模式，为大脑启发计算与生物相容性和高能效的硬件。长期目标是开发低成本，节能，高度可调，模块化，容错和自我修复的生物分子神经网络和组织。预期应用包括信号处理、传感器内和近传感器计算、神经工程、人工智能和后硅技术。教育影响利用了这个项目与工程，生物，物理和化学主题接口的事实。参与的学生将接受独家的科学培训，这将有助于他们为在多个领域做出贡献做好准备。传统的水库计算机使用水库层，其包括具有随机分配的突触权重的神经元的递归连接，随后是读出层，其非易失性突触权重被训练。假设是，可以使用表现出并置的易失性和非易失性存储器的ICM将储层和读出层组合成单个层。这些装置中的基本元件是模拟生物膜的组成和功能的绝缘脂质膜。在电压激活的离子通道的存在下，这些合成的脂质膜可以表现出由膜和离子通道动力学引起的电压依赖性忆阻。本研究的具体目标是：1）了解如何利用ICM的特定属性进行储层计算; 2）设计适合于我们的设备在储层和读出层中的非线性短期记忆动力学的架构，并可能将它们联合收割机组合到单个层中; 3）使用ICM的交叉阵列进行实验验证;以及4）推广新的储存器结构使得其可以与其它非易失性和易失性忆阻器一起使用，该项目由计算和通信基础部（CCF）的软件和硬件基础集群共同资助。计算机和信息科学与工程局（CISE）和刺激竞争研究的既定计划（EPSCoR）该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。