NER/SNB: Implementing Nano-scale, Hierarchical, Distributed Memories with CMOL (Cmos / MOLecular) Circuits

摘要：提案编号：508533标题：NER/SNB用CMOL（Cmos / MOLecular）电路实现纳米级、分级、分布式存储器PI：Dan Hammerstrom，Co-PI：Jack McCarthy Inst：俄勒冈州健康科学大学OGI科学与工程学院BME系&该项目研究了在混合CMOS /分子电路技术中创建生物启发的电路模型。 本研究基于两个关键假设：（1）稀疏、分层、分布式数据存储器（HDM）将成为智能信号处理的重要组成部分;（2）将这些模型映射到分子尺度电路将比映射到更传统的计算更有效。HDM形成整个研究计划的主要目标。 作为计算模型，它们具有与传统模型截然不同的特点。 它们更多地依赖于并行计算，而不是原始速度。 它们是异步的并且精度低。 它们是容错的。 由于这些原因，分子尺度电子学似乎是这些模型的一个有前途的计算基板。 为了发展HDM技术，我们正在关注一些重要的问题：1）开发网络模型，展示分层数据不变性和分布式表示的抽象;2）将时间信息添加到表示中;3）将具有互补功能多样性的多个HDM组件的软件系统构建为复杂的“认知”系统;4）扩展到大型网络;5）研究硬件加速; 6）将HDM与现有的智能计算技术相结合，特别是数字信号处理和基于规则的系统; 7）将HDM技术应用于真实的世界应用。 特别是，我们将着眼于混合CMOS /分子技术，CMOL，和CrossNet实现架构，这是正在开发的纽约州立大学斯托尼布鲁克教授Likharev and is group. CMOL电路的基本思想是结合联合收割机的优势，目前占主导地位的CMOS技术（包括其灵活性和高制造产量）与单电子分子器件与纳米尺度的足迹。这些器件将在预制的纳米线阵列织物上自组装，从而以可接受的制造成本实现非常高的功能密度。 CMOL技术似乎唯一地适合于实现神经形态网络的分布式交叉开关（“CrossNet”）家族，其可以允许非常有效地实现HDM。

Abstract:Proposal no: 508533Title: NER/SNB Implementing Nano-scale, Hierarchical, Distributed Memories with CMOL (Cmos / MOLecular) CircuitsPI: Dan Hammerstrom, Co-PI: Jack McCarthyInst: BME Department, OGI School of Science and Engineering Oregon Health & Science UniversityThe project examines the creation of biologically inspired circuit models in a hybrid CMOS / Molecular circuit technology. This research is based on two key assumptions, (1) that sparse, hierarchical, distributed data memories (HDMs) will be an important component of Intelligent Signal Processing, and (2) that the mapping of these models to molecular scale circuitry will be more effective than the mapping of more traditional computation.The HDMs form the primary objective of the overall research program. As computational models, they have very different characteristics from traditional models. They rely more on parallel computation than on raw speed. They are asynchronous and of low precision. And they are fault tolerant. For these reasons molecular scale electronics appear to be a promising computational substrate for these models. To develop HDM technology, we are focusing on a number of important problems:1) Developing network models that demonstrate hierarchical data invariance and abstractions with distributed representations;2) Adding temporal information to the representation;3) Building software systems of multiple HDM components with complementary functional diversity into complex "cognitive" systems;4) Scaling to large networks;5) Studying hardware acceleration;6) Integrating HDMs with existing intelligent computing technologies, in particular digital signal processing and rule-based systems; and7) Applying HDM technology to real world applications.The research performed here focuses on potential hardware acceleration with hybrid micro/nanotechnology. In particular, we will be looking at a hybrid CMOS / Molecular technology, CMOL, and the CrossNet implementation architecture, which are being developed at SUNY Stony Brook by Prof. Likharev and is group.The basic idea of CMOL circuits is to combine the advantages of the currently dominant CMOS technology (including its flexibility and high fabrication yield) with those of single-electron molecular devices with nanometer-scale footprint. The devices would be self-assembled on a pre-fabricated nanowire array fabric, enabling very high function density at acceptable fabrication cost. The CMOL technology seems to be uniquely suited for the implementation of the distributed-crossbar ("CrossNet") family of neuromorphic networks which may allow a very effective implementation of HDMs.