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: 2403559
• 负责人: Md Sakib Hasan
• 金额: $ 26.99万
• 依托单位: University of Mississippi
• 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=2403559&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严重的局限性，这可以通过采用更多脑启发的方法来解决。然而，尽管最近在人工智能算法方面取得了重大进步，但仍在开发能够模拟大脑功能和能源效率的硬件方面的大量工作。该项目旨在开发新型的物理储层计算体系结构，以利用基于离子通道的备忘录（ICMS）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特性。这将导致具有生物相容性且能节能高的硬件的新范式，用于脑启发的计算。长期目标是开发低成本，节能，高度可调，模块化，容忍和自我修复生物分子神经网络和组织。预期的应用包括信号处理，发射和近传感器计算，神经工程，人工智能和后硅技术。教育影响利用了该项目与工程，生物学，物理和化学方面的主题相结合的事实。参与参与的学生将接受独家的科学培训，这将有助于他们在多个领域做出贡献的准备。传统的储层计算机使用储层层，其中包括神经元与随机分配的突触重量的复发连接，然后是读取非电力突触权重的读取层。假设是，储层和读出层都可以使用表现出挥发性和非挥发性纪念的ICM结合到单层中。这些设备中的基本元素是模仿生物膜的组成和功能的绝缘脂质膜。在存在电压激活的离子通道的情况下，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的循环。这项提出的研究专门针对：1）了解如何利用ICM的特定特性用于储层计算； 2）设计构造是根据储层和读取层中设备的非线性短期内存动力学定制的，并可能将它们组合成单层； 3）使用ICMS的横梁阵列实验验证；和4）概括新的储层架构，以便与其他非挥发性和波动性的回忆录一起使用，可能是本质上的固态。该项目由计算机和通信基金会（CCF）的软件和硬件基础集群共同资助，该项目在计算机和信息科学和工程学方面（CISE）（CISE）（CISE）（CISE）竞争（CISE）（CISE）竞争（CISE）（CISE）竞争（CISE）。法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准来评估的值得支持的。