EMT/MISC Nanogrid Implementation of

NSF 08-517 - Emerging Models and Technologies for Computation（EMT）EMT/MISC Nanogrid Implementation of Massively Parallel Arms主要研究者：Dan Hammerstrom，PSU;合作研究者：Richard格兰杰，达特茅斯;合作研究者：Konstantin Likharev，SUNY Stony摘要我们的目标是研究使用全新的实现技术，通过新的算法和新的架构和设计技术来增强智能计算（IC）。 我们将这项工作集中在计算机视觉中的识别问题上。 我们正在研究的算法起源于神经科学，但它们是这些算法的重要抽象，其目标是保留计算的本质，同时丢弃许多生物学细节。 第一个假设是，这些算法构成了一个有前途的方法，以实现提高水平的IC。 第二个假设是，扩展到非常大的网络是智能计算的必要要求，我们使用的算法确实可以扩展。 第三个假设是CMOS永远不会以合理的成本/性能为这些算法的缩放实现提供我们所需的算法缩放。 因此，我们需要移动到密度更大的介质。我们的第四个假设是，混合CMOS /纳米网格（CMOL）CMOL是最有前途的实现技术在地平线上，因此，这里提出的研究的目标是实现大规模并行，基于统计的算法在CMOL，这是我们的解决方案的一般问题。 我们的方法是采取一个真实的应用程序与一些不同的算法阶段，并研究该阶段的映射到CMOL。 每个阶段的设计范围将基于我们称为虚拟化的概念。 特定实现的性能/价格由虚拟化程度决定，而虚拟化程度又由算法及其动态行为决定。

NSF 08-517 - Emerging Models and Technologies for Computation (EMT)EMT/MISC Nanogrid Implementation ofMassively Parallel AlgorithmsPI: Dan Hammerstrom, PSU; Co-PI: Richard Granger, Dartmouth;Co-PI: Konstantin Likharev, SUNY Stony BrookAbstractOur goal is to investigate the use of radically new implementation technology to enhance Intelligent Computing (IC) with new algorithms and new architectural and design techniques. We are focusing this work on recognition problems in computer vision. The algorithms we are studying have their origin in neuroscience, but they are significant abstractions of those algorithms, with the goal of retaining the essence of the computation while dropping many of the biological details. The 1st assumption is that these algorithms constitute a promising approach to achieving improved levels of IC. The 2nd assumption is that scaling to very large networks is a necessary requirement of intelligent computing, and the algorithms we are using do scale. The 3rd assumption is that CMOS will never give us the algorithm scaling we need at a reasonable cost/performance for scaled implementations of these algorithms. Thus we need to move to a far denser medium. Our 4th assumption then is that hybrid CMOS / nanogrids (CMOL)CMOL is the most promising implementation technology on the horizon.Consequently the goal of the research proposed here is to implement massively parallel, statistically based algorithms in CMOL, which is our solution to the general problem. Our approach is to take a real application with a number of different algorithmic stages and study the mapping of that stage to CMOL. The design spectrum for each stage will be based on a concept we call virtualization. The performance/price of a particular implementation is determined by the degree of virtualization, which in turn is determined by the algorithm and its dynamic behavior.