NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc

Center for Aeronautics and Space Information Sciences

This report summarizes the research done during 1991/92 under the Center for Aeronautics and Space Information Science (CASIS) program. The topics covered are computer architecture, networking, and neural nets

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

Status and Future Perspectives for Lattice Gauge Theory Calculations to the Exascale and Beyond

In this and a set of companion whitepapers, the USQCD Collaboration lays out a program of science and computing for lattice gauge theory. These whitepapers describe how calculation using lattice QCD (and other gauge theories) can aid the interpretation of ongoing and upcoming experiments in particle and nuclear physics, as well as inspire new ones.Comment: 44 pages. 1 of USQCD whitepapers

The distributed ASCI supercomputer project

The Distributed ASCI Supercomputer (DAS) is a homogeneous wide-area distributed system consisting of four cluster computers at different locations. DAS has been used for research on communication software, parallel languages and programming systems, schedulers, parallel applications, and distributed applications. The paper gives a preview of the most interesting research results obtained so far in the DAS project

The upsurge of deep learning for computer vision applications

Artificial intelligence (AI) is additionally serving to a brand new breed of corporations disrupt industries from restorative examination to horticulture. Computers can’t nevertheless replace humans, however, they will work superbly taking care of the everyday tangle of our lives. The era is reconstructing big business and has been on the rise in recent years which has grounded with the success of deep learning (DL). Cyber-security, Auto and health industry are three industries innovating with AI and DL technologies and also Banking, retail, finance, robotics, manufacturing. The healthcare industry is one of the earliest adopters of AI and DL. DL accomplishing exceptional dimensions levels of accurateness to the point where DL algorithms can outperform humans at classifying videos & images. The major drivers that caused the breakthrough of deep neural networks are the provision of giant amounts of coaching information, powerful machine infrastructure, and advances in academia. DL is heavily employed in each academe to review intelligence and within the trade-in building intelligent systems to help humans in varied tasks. Thereby DL systems begin to crush not solely classical ways, but additionally, human benchmarks in numerous tasks like image classification, action detection, natural language processing, signal process, and linguistic communication process

Direct N-body Simulations

Special high-accuracy direct force summation N-body algorithms and their relevance for the simulation of the dynamical evolution of star clusters and other gravitating N-body systems in astrophysics are presented, explained and compared with other methods. Other methods means here approximate physical models based on the Fokker-Planck equation as well as other, approximate algorithms to compute the gravitational potential in N-body systems. Questions regarding the parallel implementation of direct ``brute force'' N-body codes are discussed. The astrophysical application of the models to the theory of relaxing rotating and non-rotating collisional star clusters is presented, briefly mentioning the questions of the validity of the Fokker-Planck approximation, the existence of gravothermal oscillations and of rotation and primordial binaries.Comment: 32 pages, 13 figures, in press in Riffert, H., Werner K. (eds), Computational Astrophysics, The Journal of Computational and Applied Mathematics (JCAM), Elsevier Press, Amsterdam, 199