1,792 research outputs found
Looking into the Environmental Factors Affecting the Performance of Ubiquitous Technologies Deployment: An Empirical Study on Chinese Information and Communication Technology Companies
Effective deployment of ubiquitous technologies can help companies improve the business efficiency, especially for those ICT (Information communication technology) companies who are involved in M-business, M-commerce, and etc. However, there are many factors could affect the performance of the ubiquitous technologies deployment, such as the company’s management, the employee’s coordination, and etc. In this paper, we are focused on the environmental factors that would have an impact on the performance of organizations which have deployed or is deploying ubiquitous technologies, and investigate more than 50 Chinese ICT companies. According to our findings, in the context of China, a sensible, dependent, and interactive business relationship with the outside environment will have a positive impact on their ubiquitous technologies deployment’s performance, while the decentralization and hierarchism within organizational structure in the inside environment will have a negative impact on their ubiquitous technologies deployment’s performance
Precisely aligned graphene grown on hexagonal boron nitride by catalyst free chemical vapor deposition
To grow precisely aligned graphene on h-BN without metal catalyst is
extremely important, which allows for intriguing physical properties and
devices of graphene/h-BN hetero-structure to be studied in a controllable
manner. In this report, such hetero-structures were fabricated and investigated
by atomic resolution scanning probe microscopy. Moirre patterns are observed
and the sensitivity of moirre interferometry proves that the graphene grains
can align precisely with the underlying h-BN lattice within an error of less
than 0.05 degree. The occurrence of moirre pattern clearly indicates that the
graphene locks into h-BN via van der Waals epitaxy with its interfacial stress
greatly released. It is worthy to note that the edges of the graphene grains
are primarily oriented along the armchair direction. The field effect mobility
in such graphene flakes exceeds 20,000 cm2/V.s at ambient condition. This work
opens the door of atomic engineering of graphene on h-BN, and sheds light on
fundamental research as well as electronic applications based on graphene/h-BN
hetero-structure.Comment: 22 pages, 4 figures, the supporting information is also include
Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data
Applying machine learning (ML) in design flow is a popular trend in EDA with
various applications from design quality predictions to optimizations. Despite
its promise, which has been demonstrated in both academic researches and
industrial tools, its effectiveness largely hinges on the availability of a
large amount of high-quality training data. In reality, EDA developers have
very limited access to the latest design data, which is owned by design
companies and mostly confidential. Although one can commission ML model
training to a design company, the data of a single company might be still
inadequate or biased, especially for small companies. Such data availability
problem is becoming the limiting constraint on future growth of ML for chip
design. In this work, we propose an Federated-Learning based approach for
well-studied ML applications in EDA. Our approach allows an ML model to be
collaboratively trained with data from multiple clients but without explicit
access to the data for respecting their data privacy. To further strengthen the
results, we co-design a customized ML model FLNet and its personalization under
the decentralized training scenario. Experiments on a comprehensive dataset
show that collaborative training improves accuracy by 11% compared with
individual local models, and our customized model FLNet significantly
outperforms the best of previous routability estimators in this collaborative
training flow.Comment: 6 pages, 2 figures, 5 tables, accepted by DAC'2
MSREP: A Fast yet Light Sparse Matrix Framework for Multi-GPU Systems
Sparse linear algebra kernels play a critical role in numerous applications,
covering from exascale scientific simulation to large-scale data analytics.
Offloading linear algebra kernels on one GPU will no longer be viable in these
applications, simply because the rapidly growing data volume may exceed the
memory capacity and computing power of a single GPU. Multi-GPU systems nowadays
being ubiquitous in supercomputers and data-centers present great potentials in
scaling up large sparse linear algebra kernels. In this work, we design a novel
sparse matrix representation framework for multi-GPU systems called MSREP, to
scale sparse linear algebra operations based on our augmented sparse matrix
formats in a balanced pattern. Different from dense operations, sparsity
significantly intensifies the difficulty of distributing the computation
workload among multiple GPUs in a balanced manner. We enhance three mainstream
sparse data formats -- CSR, CSC, and COO, to enable fine-grained data
distribution. We take sparse matrix-vector multiplication (SpMV) as an example
to demonstrate the efficiency of our MSREP framework. In addition, MSREP can be
easily extended to support other sparse linear algebra kernels based on the
three fundamental formats (i.e., CSR, CSC and COO)
Advancing Agro-Based Research
Taking the next sums up Universiti Putra Malaysia (UPM) approach to research. The university now aims to create an environment that inspires innovative research following its selection as a research university by the Higher Education Ministry in November 2006
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