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

摘要：提案号：508533标题：NER/SNB实现纳米级，分层，分布式存储器与CMOL （Cmos /分子）电路spi: Dan Hammerstrom， Co-PI: Jack mccarthyist：俄勒冈健康与科学大学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（HDMs）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更传统的计算更有效。HDMs构成了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，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。为了开发HDM技术，我们专注于一些重要问题：1)开发网络模型，展示分层数据不变性和具有分布式表示的抽象；2)在表示中加入时间信息；3)将功能多样性互补的多个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)硬件加速研究；6)集成HDMs与现有的智能计算技术，特别是数字信号处理和基于规则的系统；7)将HDM技术应用于现实世界。这里进行的研究主要集中在混合微/纳米技术的潜在硬件加速。特别地，我们将关注由Likharev教授及其团队在纽约州立大学石溪分校开发的混合CMOS /分子技术，CMOL和CrossNet实现架构。CMOL电路的基本思想是将目前占主导地位的CMOS技术的优点（包括其灵活性和高制造成品率）与纳米级单电子分子器件的优点相结合。这些设备将在预制的纳米线阵列织物上自组装，在可接受的制造成本下实现非常高的功能密度。CMOL技术似乎特别适合于分布式交叉棒（“CrossNet”）神经形态网络家族的实现，这可能允许非常有效地实现HDMs。

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.