CDI-Type II: Inference at the Nanoscale

CDI-Type II：纳米级推理

• 批准号: 1028378
• 负责人: Christof Teuscher
• 金额: $ 64.75万
• 依托单位: Portland State University
• 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 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1028378&HistoricalAwards=false
• 关键词: CDI; Type; II; Inference; Nanoscale

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.

该项目的目标是开发一种新的基于推理的信息处理结构，使用全新的纳米级设备执行概率计算。 这种方法利用了这种设备的模拟、时间依赖性及其大量并行性。 通过这样做，这样的计算结构将比使用更传统的数字硬件更有效和可扩展。 这种方法是第一种包括时间相关电路元件来构建近似贝叶斯推理的模拟关联存储器的方法之一，并且这些模拟关联存储器反过来被组装成复杂网络，以捕获数据流中的高阶结构。 最终的目标是使用这些电路开发混合CMOS /分子规模实现的现场自适应贝叶斯阵列（FABA），这有可能成为一个关键组成部分的网络使能的发现。网络使能的发现在这项研究中解决了两个方面。 第一个涉及的模拟电路的设计的基础上复杂的纳米和分子尺度的设备随时间变化的属性。第二个问题涉及创建新的半导体元件系列，这将显着增强广泛数据和应用中的网络支持发现应用。设计通过空间和时间进行推理并由动态元件组成的模拟纳米电子电路（例如记忆电阻和记忆电容）非常困难。当人们考虑到世界各地的实验室正在为纳米和分子尺度电子学开发的各种复杂设备时，这一点尤其如此。 为此，我们定义了一种结合了多层次抽象和可进化计算的探索方法，两个关键的发展是针对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并行架构。 这项研究将探索一个新的范例，使用纳米电子的新兴应用程序开始与“自上而下”的系统要求，而不是通过寻找新的设备概念的应用程序（“自下而上”）。作为半导体行业的斗争，在哪里去下一个，这里提出的工作可能会提供深入了解激进的新方法架构，电路和设备。 这项研究最终将通过增强人类认知和从社会必须处理的异构数字数据财富中产生新知识来造福社会。