Abstract:This paper examines industry-based doctoral dissertation research in a professional computing doctoral program for full time working professionals through the lens of different machine learning algorithms to understand topics explored by full time working industry professionals. This research paper examines machine learning algorithms and the IBM Watson Discovery machine learning tool to categorize dissertation research topics defended at Pace University. The research provides insights into differences in machine learning algorithm categorization using natural language processing.
Abstract:With the skyrocketing needs of rapid processing speed and miniaturization, classical computers are not able to keep up the pace with these few necessary parameters. As classical computers work on classical mechanics there expansion is at zenith. Due to these limitation quantum mechanics is taking the role of game changer in race of computation. Quantum computing studies quantum computer with the quantum mechanics phenomenon of superposition, entanglement, tunneling and annealing to solve problems that can’t be solved in life span of human beings. This review aims for summarizing timeline of the quantum computing along with quantum mechanics behind it.
Abstract:This paper presents a software certification project, developed for a government agency. The appeal of this case is that software certification is incipient in Latin America, and is the first time that IRAM Chile, in collaboration with IRAM Argentina (Argentinian National Standardization Institute) certified governmental software. The International Standard ISO /IEC 25040 has also been taken as a basis in the certification process. It also demonstrates an example of the collaborative synergy between the educational environment (Universities) and the private sector (testing laboratories), taking advantage from both.
Published in: IEEE CACIDI 2016 – IEEE Conference on Computer Sciences
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Many companies are deploying services largely based on machine-learning algorithms for sophisticated processing of large amounts of data, either for consumers or industry. The state-of-the-art and most popular such machine-learning algorithms are Convolutional and Deep Neural Networks (CNNs and DNNs), which are known to be computationally and memory intensive. A number of neural network accelerators have been recently proposed which can offer high computational capacity/area ratio, but which remain hampered by memory accesses. However, unlike the memory wall faced by processors on general-purpose workloads, the CNNs and DNNs memory footprint, while large, is not beyond the capability of the on-chip storage of a multi-chip system. This property, combined with the CNN/DNN algorithmic characteristics, can lead to high internal bandwidth and low external communications, which can in turn enable high-degree parallelism at a reasonable area cost. In this article, we introduce a custom multi-chip machine-learning architecture along those lines, and evaluate performance by integrating electrical and optical inter-chip interconnects separately. We show that, on a subset of the largest known neural network layers, it is possible to achieve a speedup of 656.63× over a GPU, and reduce the energy by 184.05× on average for a 64-chip system. We implement the node down to the place and route at 28 nm, containing a combination of custom storage and computational units, with electrical inter-chip interconnects.
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Erik P. DeBenedictis, Sandia National Laboratories
Will quantum computation become an
important milestone in human progress?
Passionate advocates and equally passionate
skeptics abound. IEEE already provides useful,
neutral forums for state-of-the-art science
and engineering knowledge as well as practical
benchmarks for quantum computation
evaluation. But could the organization do more?
COMPUTER PUB L ISHED B Y THE I EEE COMPUTER SOCI E T Y