Collaborative Research: CDI: Inference at the Nano-Scale

合作研究：CDI：纳米级推理

• 批准号: 1028336
• 负责人: Dmitri Strukov
• 金额: $ 39.21万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1028336&HistoricalAwards=false
• 关键词: Collaborative; Research; CDI; Inference; Nano

The goal of this project is to develop a new inference-based information processing structure that performs probabilistic computing using radically new nanoscale devices. This approach exploits the analog, time-dependent properties of such devices, and their massive parallelism. By doing so, such a computing structure will be more efficient and scalable than by using more traditional digital hardware. This approach is one of the first to include time-dependent circuit elements to build analog associative memories that approximate Bayesian inference, and which are, in turn, assembled into complex networks that capture higher order structure in streams of data. The ultimate goal is to use these circuits to develop hybrid CMOS / molecular scale implementations of a Field Adaptable Bayesian Array (FABA), which has the potential to be a key component for Cyber-Enabled discovery.Cyber-Enabled discovery is addressed in this research in two ways. The first concerns the design of analog circuits based on complex nano and molecular scale devices with time-varying properties. And the second concerns the creation of a new family of semiconductor components that will significantly enhance Cyber-Enabled discovery applications across a wide range of data and applications.Designing analog nano-electronic circuits that perform inference through space and time and which consist of dynamic components (such as mem-resistance and mem-capacitance) is extraordinarily difficult. This is particularly true when one considers the wide range of complex devices that are being developed in laboratories around the world for nano and molecular scale electronics. For this effort we have defined an Exploration Methodology that combines multiple levels of abstraction and evolvable computation.Two key developments then are a design exploration methodology for such devices, and a massively parallel architecture for data capture and inference. This research will explore a new paradigm for using nanoscale electronics for emerging applications by starting with the "top-down" system requirements rather than by finding applications for new device concepts ("bottom-up").As the semiconductor industry struggles with where to go next, the work proposed here may provide insight into radical new approaches to architecture, circuits and devices. This research will ultimately benefit society by enhancing human cognition and generating new knowledge from the wealth of heterogeneous digital data society has to deal with.

该项目的目的是开发一种新的基于推理的信息处理结构，该结构使用根本新的纳米级设备执行概率计算。 这种方法利用了此类设备的模拟，时间依赖性特性及其庞大的并行性。 通过这样做，这种计算结构将比使用更多传统的数字硬件更有效，可扩展。 这种方法是最早包含时间相关电路元素来构建近​​似贝叶斯推论的模拟关联记忆的方法之一，这些记忆又被组装成复杂的网络，这些网络捕获了数据流中的高阶结构。 最终的目标是使用这些电路来开发田间适应能力的贝叶斯阵列（FABA​​）的混合CMO /分子尺度实现，这有可能成为支持网络基础发现的关键组成部分。基于Cyber​​ber的发现在这项研究中以两种方式解决了。 第一个涉及基于复杂的纳米和分子尺度设备的模拟电路的设计。第二个问题是创建一个新的半导体组件家族，该家族将显着增强在广泛的数据和应用程序中的网络启用的发现应用程序。设计模拟纳米纳米电路电路，这些电路可以通过时空进行推理，并且由动态组成（例如，磁性和mem抗性和磁性组成）很难。当人们考虑在世界各地的纳米和分子尺度电子产品中开发的广泛的复杂设备时，尤其如此。 为了这项工作，我们定义了一种探索方法，该方法结合了多个级别的抽象和可转化的计算。然后，关键的开发是此类设备的设计探索方法，以及用于数据捕获和推理的大规模平行体系结构。 This research will explore a new paradigm for using nanoscale electronics for emerging applications by starting with the "top-down" system requirements rather than by finding applications for new device concepts ("bottom-up").As the semiconductor industry struggles with where to go next, the work proposed here may provide insight into radical new approaches to architecture, circuits and devices. 这项研究最终将通过增强人类认知并从异质数字数据社会所能处理的财富中获得新知识来受益。