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