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

通用数字计算范式面临着严重的局限性，这可能会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量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储层计算架构，利用基于离子通道的忆阻器（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，以利用所提出材料的独特性能。这将导致一个新的范例，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、高能效、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。预期的应用包括信号处理、传感器内和近传感器计算、神经工程、人工智能和后硅技术。这个项目与工程学、生物学、物理学和化学的主题相结合，这一事实对教育产生了影响。参与的学生将接受专门的科学培训，这将有助于他们为在多个领域做出贡献做好准备。传统的存储层计算机使用一个存储层，该存储层包括随机分配突触权的神经元的循环连接，然后是一个读取层，其非易失性突触权被训练。假设储层和读出层可以使用icm组合成一个单层，这些icm具有并列的易失性和非易失性存储器。这些装置的基本元件是一个绝缘的脂质膜，它模仿生物膜的组成和功能。在电压激活离子通道存在的情况下，这些合成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ICMs的特定性质进行储层计算；2)在存储层和读出层设计适合器件非线性短期记忆动态的架构，并可能将它们组合成一个单层；3)利用交叉棒阵列对ICMs进行实验验证；4)推广新的储层结构，使其可以与其他非易失性和易失性记忆电阻器一起使用，可能是固态的。该项目由计算机与信息科学与工程理事会（CISE）计算与通信基础部（CCF）的软件和硬件基础集群以及刺激竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.