Filters: Keyword is Emerging computing  [Clear All Filters]
Hacimahmud, A. V., Khakhanova, H., Hahanov, I., Hahanov, V., Chumachenko, S., Litvinova, E., Shevchenko, O..  2020.  Structure and Metrics of Emerging Computing. 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). :920—925.

A metric and structure of computing 2020 is proposed in the form of Top 12 Technology Trends, which will influence on investment in science, education and industry in developing countries. The primary social and technological problem of the protection of society and critical facilities through the creation of Global Intelligent Cyber Security is formulated. The axioms of the constructive formation of developing countries on the basis of the adoption of moral relations are formulated. Models, methods and algorithms of cyber-social computing are proposed that are focused on processing big data, searching for keywords and test fragments. New characteristic equations of similarity - differences between the processes and phenomena are synthesized for the exact information retrieval by keywords in cyber-physical space. A computing model of the development of the Universe is formulated, where the binary interactions of entities and forms are harmonic functions of the phase state. A structure of interactive computing of the creative process based on a metric assessment of the development status with world achievements is proposed.

Imani, Mohsen, Gupta, Saransh, Rosing, Tajana.  2017.  Ultra-Efficient Processing In-Memory for Data Intensive Applications. Proceedings of the 54th Annual Design Automation Conference 2017. :6:1–6:6.

Recent years have witnessed a rapid growth in the domain of Internet of Things (IoT). This network of billions of devices generates and exchanges huge amount of data. The limited cache capacity and memory bandwidth make transferring and processing such data on traditional CPUs and GPUs highly inefficient, both in terms of energy consumption and delay. However, many IoT applications are statistical at heart and can accept a part of inaccuracy in their computation. This enables the designers to reduce complexity of processing by approximating the results for a desired accuracy. In this paper, we propose an ultra-efficient approximate processing in-memory architecture, called APIM, which exploits the analog characteristics of non-volatile memories to support addition and multiplication inside the crossbar memory, while storing the data. The proposed design eliminates the overhead involved in transferring data to processor by virtually bringing the processor inside memory. APIM dynamically configures the precision of computation for each application in order to tune the level of accuracy during runtime. Our experimental evaluation running six general OpenCL applications shows that the proposed design achieves up to 20x performance improvement and provides 480x improvement in energy-delay product, ensuring acceptable quality of service. In exact mode, it achieves 28x energy savings and 4.8x speed up compared to the state-of-the-art GPU cores.