ICT

While Artificial Intelligence (AI) emerged in the 1950s, only in recent years have AI applications proliferated with the explosion of data and continuing improvements in Moore’s law that have driven rising processing speeds. Voice assistants, image analysis software, search engines, and speech and facial recognition systems were among the first applications to use AI. Today, adoption has spread to sectors such as agriculture, cybersecurity, healthcare, software development, e-government and the intelligent enterprise to generate jobs and help spur economic growth. The Edge AI Opportunity and the Microelectronics IndustryAI can be embedded in hardware devices such as advanced robots, autonomous cars, drones or Internet of Things (IoT) applications. Today, according to the EU’s digital strategy, data centres and other centralized computing facilities account for the vast majority – 80% – of AI data processing and analysis, with smart connected objects such as automobiles, home appliances and manufacturing robots that bring the compute function closer to the user representing 20%. The latter, known as Edge AI applications, are powered by edge-based machine learning chipsets, not the AI chipsets designed to run cloud-based machine learning algorithms.The EU’s white paper on AI published in February 2020 anticipates that the way data are stored and processed for AI applications will change significantly over the coming five years as edge computing applications proliferate. Most AI applications need to connect with devices that collect data and manage data flows. When the applications connect with cloud infrastructures to train large volumes of data for a machine learning model, the interface devices often require hardware support. Edge AI can minimize data transport by processing data directly from local devices to accelerate data analysis and decision-making and make data transport or accelerator hardware unnecessary, critical in reducing power consumption and enhancing data security for applications such as autonomous driving. Over the past 40 years, the ICT sector has been continuously increasing greenhouse gas (GHG) emissions despite efforts to shift to renewable energy. Cloud-based AI applications require an ICT infrastructure for high-performance computing and high-speed connectivity. According to MIT Technology Review, data centres’ AI workloads could account for a tenth of the world’s electricity usage by 2025. a mass update of cloud-based AI applications may significantly increase energy consumption, unlike with Edge AI. This is why the strategy for developing Edge AI is well-aligned with the EU’s Green Deal objectives. Europe aspires to play a leadership role in Edge AI to strengthen the sector’s competitiveness and protect the European digital sovereignty. Europe’s strong industrial competencies in embedded systems and microcontrollers will help the region promote development of European domestic AI solutions for emerging high-value IoT applications in industrial processes such as Industry 4.0, Connected and Automated driving (CSA), smart cities, climate action, healthcare, and national defence and security. With this strong strategic position in technology, Europe is well-positioned to invest to become the leader in the Edge AI global market.Preparing the Workforce for the Microelectronics IndustryTo design and manufacture leading Edge AI chipsets, European education providers and industry will need to work closely together to train the current and future workforces. Within the framework of the METIS project, a four-year project co-funded by the European Commission through the Erasmus+ programme, SEMI and imec deployed experts in the field to survey and interview focus groups. The survey identified the following key focus areas for workforce development: 1. True Capability of AI and Data Science With AI’s heavy dependence on data, the workforce of the future must be trained in areas of data science including data integrity to ensure quality, unbiased sourcing, collection and accurate analysis necessary to interpret huge volumes of data. Europe also needs to train the next generation of AI chip designers in data security and privacy – key challenges to the widespread deployment of Edge AI chips. 2. Climate Change, Sustainable Development Goals (SDGs) and Social Inclusion TrainingSince the industry must be able to develop Edge AI solutions to enable the digital transformation while limiting GHG emissions, microelectronics engineers need to be schooled in climate change and understand how their work contributes to meeting the United Nation’s Sustainable Development Goals (SDGs). Workplace diversity and social inclusion are also important target areas for education since Edge AI applications should serve various groups of people with different needs.3. EthicsChip industry workers must also be educated in ethical issues of AI related to the technology’s potential societal impact in the near future[1]. With AI applications capable of monitoring Internet searches based on users’ personal preferences and biases to deliver tailored advertising, news and other information, developers must recognize how the technology can influence thinking and behaviour of individuals and groups. This awareness can help developers strike a balance between supporting commercial interests and societal good so the microelectronics industry can ensure ethical implementation of AI. 4. Cross-disciplinary Skills Required for AIAI development requires a comprehensive, cross-disciplinary skill-set to be able to integrate the work of specialists from diverse educational, cultural and professional backgrounds critical to developing non-biased AI solutions. For example, in addition to technical expertise, microelectronics AI developers must be able to communicate clearly and work in close-knit teams with non-technical experts from business, law, medicine and the social sciences.What’s Next?The microelectronics industry has a tremendous opportunity to develop new chip-based solutions for AI architectures, and apply AI techniques to improve operational efficiencies of design and manufacturing. To seize this opportunity, the industry must work closely with education providers to groom the next generation of skilled workers. This tight collaboration is critical to designing and delivering specialised courses to college and university students as well as engineers now working in the chip sector. The stakes are high. By preparing workers to develop Edge AI chipsets, the microelectronics industry can help the world confront some of the greatest challenges it faces today.For more information, see SEMI Responds to European Commission White Paper on Artificial Intelligence.METIS is a Sector Skills Alliance project co-funded by the European Commission’s Erasmus+ Program and coordinated by SEMI. The four year project, launched in November 2019, will develop a Microelectronics Skills Strategy. Based on the strategy, the METIS project will design 43 training modules for 1,100 hours learning in four key areas of the microelectronics sector.We thank Patrick Blouet (STMicroelectronics) and Jeroen Geusens (imec) for their valuable contributions to this article.[1] Ethics of Artificial Intelligence and Robotics, Stanford Encyclopedia of PhilosophyDr. Yanying Li is senior manager of Collaborative Projects at SEMI Europe.Dr. Pushkar P. Apte is the strategic technology advisor for the Smart Data AI Initiative at SEMI

Companies around the world are increasingly turning to mergers and acquisitions, research and development, and corporate venture capital (CVC) investment to sustain growth. For many years, global semiconductor companies including Intel, Qualcomm and Samsung have been active CVC investors. However, the economic fallout from the COVID-19 pandemic has forced many venture capital (VC) and CVC investors to rethink their investment strategies as they look to an uncertain future. To help provide SEMI members with the latest market trend information, SEMI Taiwan held the webinar Challenges and Opportunities in Corporate Venturing during the Global Pandemic Crisis on April 28th. Featured speaker James Mawson, founder and editor in chief of Global Corporate Venturing, provided an analysis of the pandemic’s impact on deal flow, capital movement, sentiment and strategies among CVCs. CVC takes larger role in past decadeCorporations have been increasingly active direct and indirect venture investors over the past decade. From 2011-2019, more than US$1.3 trillion of venture capital was invested globally, with corporations accounting for more than half that total, according to data from Pitchbook/GCV Analytics.Semiconductor companies that have been active in corporate venturing include Intel, Samsung, Nvidia, ARM, AMD, SK Hynix, Broadcom and Qualcomm. Pure-play semiconductor and chip companies tend to make few investments in their start-up counterparts because sector saturation of powerful incumbents leaves little opportunity for growth, James said. “While it is hard to find entrepreneurs wanting to be engaged in pure play S C, once they do, they can be very valuable and often be able to bring disruptive forces to the whole ecosystem,” James said.S C corporate investors focus on chip applicationsSemiconductor companies looking beyond pure-play S C start-ups for investment opportunities often target applications or developers that require the additional data, processing power, and memory their chips provide. “There is lots of interest by the big chip companies such as Intel, Qualcomm, and Samsung in developing some of those chip applications, getting them used more and creating a whole ecosystem,” James said.For example, Intel Capital, based on its data-centric theme, has focused on areas like autonomous vehicles, data centers and artificial intelligence (AI) because of the sheer amount of data and processing power they require. In another notable trend, non-traditional S C players such as Apple and Alibaba are leveraging investments in start-ups to develop their own chips for competitive advantage, James said.March deal flow down 20% With COVID-19 slowing the global economy, James expects semiconductor and chip companies to scale back direct investments this year due to rising pressure on their balance sheets. Deal flow in March was down roughly 20% from February.James is hopeful corporates will focus on investing in innovation over the long term rather than target share buybacks to boost near-term earnings. James pointed out that investors can uncover opportunities by identifying future problems to be solved in areas such as quantum computing, biotech, energy, healthcare, communications and ICT. Still, in the near term, where there is a crisis, there is opportunity. While the pandemic hit some sectors hard, it benefits start-ups in industries including gaming, education and telemedicine. This time is different?James said corporates need to rethink the investment model they want to follow. One option is the approach taken by General Electric, which divested its investment team and sold all its portfolio companies last year. Another is to focus on the long term. For example, Intel Capital has been dedicated to investments in innovation for nearly 30 years and continues to invest during downturns.Compared with the internet bubble and global financial crisis, today there are more experienced and mature CVCs that better know how to negotiate a crisis. James also pointed out investors are interested in backing CVCs with sector investing experience. There are now more than 600 CVCs with a 10-year-plus track record.James expects a variety of funding models to emerge over the next decade as pressure on corporate balance sheets encourages corporate investors to consider models that allow third-party capital to effectively leverage their CVC units. Corporate investors are also open to other ways to efficiently deliver financial returns.For more information about the SEMI Taiwan Corporate Growth and Innovation Community, please contact Irene Lin at [email protected]. For GCV’s latest news and event, visit its website.Jo-Ann Su is senior director of the Corporate Growth and Innovation Community at SEMI Taiwan.