The future of computing beyond Moore's Law.

Moore's Law is a techno-economic model that has enabled the information technology industry to double the performance and functionality of digital electronics roughly every 2 years within a fixed cost, power and area. Advances in silicon lithography have enabled this exponential miniaturization of electronics, but, as transistors reach atomic scale and fabrication costs continue to rise, the classical technological driver that has underpinned Moore's Law for 50 years is failing and is anticipated to flatten by 2025. This article provides an updated view of what a post-exascale system will look like and the challenges ahead, based on our most recent understanding of technology roadmaps. It also discusses the tapering of historical improvements, and how it affects options available to continue scaling of successors to the first exascale machine. Lastly, this article covers the many different opportunities and strategies available to continue computing performance improvements in the absence of historical technology drivers. This article is part of a discussion meeting issue 'Numerical algorithms for high-performance computational science'

Energy challenges for ICT

The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT

D2.7 Online mapping of Chinese and European ICT industrial associations – Final report

This document is a deliverable of CHOICE project task 2.2 Bringing together European and Chinese Industrial Associations. This task will, at the end of the project, have delivered a final version of an online database of relevant Chinese and European ICT industrial associations. This document is a report on the information gathered for the development of the online database of relevant Chinese and European ICT industrial associations. It maps Chinese industrial associations to European industrial associations in ICT. It does this by providing details of the Chinese industrial associations and other relevant bodies that are a Chinese priority for collaboration with their EU counterparts related to EU-China collaborative ICT R&D&I. Other such Chinese bodies that are not a Chinese priority for such collaboration that map to European industrial associations in ICT are then summarised so that a complete mapping of Chinese industrial associations and related bodies to European industrial associations in ICT can be made. Mappings are made based on the best matches in terms of the subject matter covered by the associations, in each case the best match based on this criteria is identified and highlighted by being given in bold type. If a mapped Chinese association is not a priority for collaboration it is identified in italics. The Chinese selection of Chinese bodies that are a priority for collaboration with EU counterparts reflects the relevant policy guidance provided on behalf of the Chinese government on how the aims of the "Twelfth Five-Year" National Strategic Emerging Industry Development Plan are to be achieved. An overview of the policy guidance relevant to ICT R&D&I is given in section one of CHOICE deliverable D2.4: Handbook for China ICT Industrial Support Services. Based on this mapping opportunities to foster interaction between the associations in China and Europe are identified. A DVD of the modest database that exists on the basis of the mapping presented in this report accompanies the report

AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes

Smart Manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying Industrial Internet of Things (IIoT) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management. Embracing Machine Learning and Artificial Intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on Evolutionary Computing and Deep Learning algorithms toward making semiconductor manufacturing smart. We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.Comment: 13 pages, 7 figures. To appear in IEEE/CAA JA

CHOICE_ WP2_D2.1_ Online mapping of Chinese and European ICT industrial associations

Funded by the 7th Framework Programme of the European Union. Grant Agreement: 61057