US-Belgium workshop: Atomic Switch Networks for Neuromorphic Reservoir Computing; Late Fall-2015/Early Spring 2016; University of Ghent-Belgium.

美国-比利时研讨会：用于神经形态储层计算的原子交换网络；

• 批准号: 1444214
• 负责人: Adam Stieg
• 金额: $ 4.91万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2017-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1444214&HistoricalAwards=false
• 关键词: US; Belgium; workshop; Atomic; Switch

NSF CNIC proposal #1444214US-Belgium Workshop: Atomic Switch Networks for Neuromorphic Reservoir ComputingPart 1:The human brain outperforms digital computers in a number of tasks such as image, motion tracking and sound recognition and decision making in complex and often noisy and error prone environments. Digital computers are by their nature poorly-suited for tasks such as autonomous control (navigation, robotics), pattern recognition (speech, vision) or prediction (weather, financial markets). A biologically inspired approach to computing, called Reservoir Computing (RC), on the other hand, has demonstrated the potential to perform complex tasks efficiently. To perform RC, a newly developed hardware platform called the Atomic Switch Network (ASN) uses nanotechnology to create billions of synthetic synapses wired up in a fashion similar to that of the neocortex in the human brain. The implementation of a functioning RC-ASN system requires the collaborative expertise from recognized world leaders in RC methods at Ghent University, Belgium and the UCLA team who have developed the ASN device. UCLA has proposed a participant-driven workshop involving invited lectures, hands-on tutorials with hardware and software and breakout discussions with the goal to accelerate realization of this new form of computers system. This workshop will provide international research opportunities to 5 US students and early career researchers, while also promoting team-building skills, student-driven collaboration, and cultural exchange. By combining concepts from nanoscience, neuroscience, and machine learning, this proposal seeks to leverage the collective expertise of all parties to advance this next-generation cognitive technology. The successful outcomes of this research will also benefit the BRAIN Initiative, which is a priority research area of the U.S. Part 2:Atomic Switch Networks (ASN) are a unique class of biologically inspired computing architectures designed to produce a complex, dynamical system through the collective interactions of functional nanoscale materials. These self-organized devices retain the intrinsic memory capacity of their component resistive switching elements while generating a class of emergent behaviors commonly associated with biological cognition. Their capacity for non-linear transformation of input information, which is processed and stored in a distributed fashion, generates patterns of dynamic spatiotemporal activity that can be used as the basis for a computational platform. Recent efforts to model, simulate, and measure the operational dynamics of ASNs toward hardware implementation of reservoir computing (RC), a burgeoning field that investigates the computational aptitude of complex biologically inspired systems to address problems in which data is constantly changing, incomplete, or subject to errors, indicate the necessity to establish a collaboration with experts in the field of machine learning. The combined expertise of proposed workshop participants will focus on a critical assessment of how to best utilize ASN devices to overcome current operational limits on real-time signal processing in the RC paradigm such as speed, network density, and scalability. Beyond lectures and discussion sections, tutorial workshops delivered by participants from the US and EU will be utilized to disseminate/demonstrate the current status of (1) modeling/simulation of ASNs, (2) physical implementations of ASNs, and (3) physical implementations of other hardware systems (memristors, optoelectronics, etc.). Targeted outcomes include identification of specific areas for near-term collaboration and follow-on funding within existing Core programs at the NSF. This new collaboration will provide a tremendous opportunity to explore the best-case scenario resulting from the world's leading RC research with a potentially groundbreaking platform for hardware-based RC to contribute to novel approaches in real-time information processing and computation.

NSF CNIC提案＃1444214US Belgium研讨会：神经形态储层计算机的原子开关网络1：人脑在许多任务中胜过数字计算机，例如图像，运动跟踪和声音识别和在复杂且通常是贵族和错误的环境中的声音识别和决策。数字计算机本质上不适合诸如自主控制（导航，机器人技术），模式识别（语音，视觉）或预测（天气，金融市场）等任务。另一方面，一种具有生物学启发的计​​算方法，称为储层计算（RC），已经证明了有效执行复杂任务的潜力。为了执行RC，一个名为Atomic Switch Network（ASN）的新开发的硬件平台使用纳米技术来创建数十亿个综合突触，以类似于人类大脑中新皮层的方式连接起来。实施功能正常的RC-ASN系统需要来自比利时根特大学RC方法的公认世界领导者和开发ASN设备的UCLA团队的协作专业知识。加州大学洛杉矶分校（UCLA）提出了一个参与者驱动的研讨会，其中涉及邀请的讲座，带有硬件和软件的动手教程以及突破性讨论，目的是加速这种新形式的计算机系统。该研讨会将为5名美国学生和早期职业研究人员提供国际研究机会，同时还促进团队建设技能，学生驱动的合作和文化交流。通过结合纳米科学，神经科学和机器学习的概念，该建议旨在利用各方的集体专业知识来推进这种下一代认知技术。这项研究的成功结果也将使大脑计划受益，这是美国第2部分的优先研究领域：原子交换机网络（ASN）是一类独特的生物学启发的计​​算体系结构，旨在通过该系统生产一个复杂的动态系统功能性纳米级材料的集体相互作用。这些自组织的设备保留了其组件电阻切换元件的内在记忆能力，同时产生了一类通常与生物认知相关的新兴行为。它们以分布式方式处理和存储的输入信息的非线性转换能力生成动态时空活动的模式，可以用作计算平台的基础。最近的努力，以建模，模拟和衡量ASN向硬件实施储层计算（RC）的操作动力学（RC），这是一个新兴的领域，研究了复杂的生物学启发系统的计算能力，以解决数据不断变化，不完整或不完整或不完整的问题在错误的情况下，表明有必要与机器学习领域的专家建立合作。提议的研讨会参与者的综合专业知识将重点介绍如何最好地利用ASN设备来克服RC范式中实时信号处理的当前操作限制，例如速度，网络密度和可扩展性。除了讲座和讨论部分之外，美国和欧盟参与者提供的教程研讨会将被用于传播/演示（1）ASN建模/仿真的当前状态，（2）ASN的物理实现，（3）物理实施。其他硬件系统（Memristors，Optoelectronics等）。有针对性的结果包括识别NSF现有核心计划中的近期协作和后续资金的特定领域。 这项新的合作将为探索世界领先的RC研究带来的最佳场景提供一个巨大的机会，该研究具有一个潜在的基于硬件的RC的开创性平台，可以为实时信息处理和计算中的新方法做出贡献。