edge computing

For the semiconductor industry to flourish, all chip ecosystems must embrace closer ties, greater openness, and more flexibility to co-innovate solutions in today’s rapidly unfolding global digital transformation.

Today, AI algorithms are primarily run at large data centers-- in the cloud. For this intelligence to be used at the edge, data must be transmitted to the cloud, analyzed there, and the results transmitted back to the field of operation, such as a mobile phone, medical implant or autonomous vehicle.

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

The more than 53,000 people who flocked to SEMICON Korea last month were treated to a motherlode of insight into the future of the semiconductor industry as 470 companies exhibited innovative technologies in more than 2,000 booths. But the annual event’s most arresting numbers came in keynotes and other presentations pointing to the extraordinary industry growth that lies ahead.“It is no exaggeration to say that 90 percent of the world’s data has been generated in the last few years,” said Jim Feldhan, president of Semico Research. “This explosive growth of data is expected to continue. That's why server shipments will grow by 20.3 percent, or 30 million units, this year alone.”Feldhan said that the Internet of Things (IoT) will be a chief driver of semiconductor industry growth, with IoT expected to be applied in areas as varied as automotive, smart cities, edge computers, finance, architecture, agriculture and healthcare. For its part, artificial intelligence (AI) will start to exercise human-like judgment. Feldhan noted that in many instances in these fields, “it is more accurate to apply AI and vision systems than to rely on traditional decision-making.”Yoon Jong Lee, senior vice president of DB HiTek, predicted that the Internet, AI and 5G will drive market growth. “Looking back over the past 30 years, semiconductor market growth was powered by PCs, the Internet and cell phones, yet last year memory accounted for 35 percent of total semiconductor sales, more than double the figure in 2016,” he said. He predicted that, in 2019, the foundry sector will outstrip the semiconductor market in growth, noting that the average growth rate of the semiconductor industry is expected to be 4.1 percent, compared to 7.1 percent for the foundry market. Clark Tseng, director of SEMI, reported that the strong semiconductor growth in 2018 is unlikely to continue in 2019 due to the decline in memory pricing, as well as mobile and PC demand. “Demand for semiconductors is likely to decline in the first half as the industry is still digesting inventory and rebound in the second,” Tseng said. Semiconductor industry growth headwinds include decreases in high-end smartphone purchases, PC demand and demand for DRAMs for servers in data centers, Tseng said. Declines in economic growth and consumption in China and the U.S.-China trade war will also contribute to a slowdown. However, Tseng noted that, over the long term, technology innovation will continue and that the semiconductor industry’s prospects remain bright.One key innovation will be the elimination of AI’s reliance on Internet connections in the future. In his opening day keynote, Eunsoo Shim, senior vice president at Samsung Electronics, emphasized that AI technology that operates without the Internet in the future is essential. “We are developing 'on-device AI' technology that incorporates AI algorithms in products such as smartphones and autonomous vehicles,” he said. "When on-device AI technology is implemented, it reduces reliance on the Internet, battery consumption, and data latency.” Reducing latency will significantly improve device response time.Walden C. Rhines, CEO Emeritus of Mentor, a Siemens business, predicted that AI will fuel rapid memory growth. The memory semiconductor (DRAM, NAND flash) market is expected to see a temporary slowdown this year, with the market expected to rebound in 2020. Rhines said that memory could be seen as an early market with rapid future growth, citing memory market super-booms in 1995 and 2000.“Memory production has not decreased since 1995 or 2000,” he said. “Although memory prices will temporarily fall this year after significant market growth in 2017 to 2018, the market will continue to grow as memory production increases,” he said. Rhines added that “although memory prices will drop by about 10 percent this year, he believes prices will increase 6 percent next year.” He also predicted the steady growth of the non-memory semiconductor market as AI technology matures and China’s investment in fabless companies continues.Indeed, SEMICON Korea speakers made it clear that concerns about the growth of the semiconductor industry are expected to be short-lived. While overall growth is likely to slow in 2019, the industry is expected to rebound steadily – powered by the semiconductor industry paradigm shift led by AI, IOT, and autonomous driving – and reach a new high of nearly $541 billion in 2020.Jaegwan Shim is a marketing specialist at SEMI Korea.

As artificial intelligence’s (AI) sprawling influence reshapes industries from logistics and healthcare to automotive and manufacturing, Taiwan is poised to leverage its cutting-edge capabilities and rich history in semiconductor manufacturing to stake out a leadership position in AI. Taiwan’s semiconductor manufacturing industry accounts for a major share of the region’s GDP and, with its manufacturing prowess, the region is fertile ground for using AI to optimize and even revolutionize chip manufacturing. In an AI and Semiconductor Smart Manufacturing Forum recently hosted by SEMI Taiwan, experts from Micronix, Advantech, Nvidia and the Ministry of Science and Technology of Taiwan (MOST) shared their insights on how deep learning, data analytics and edge computing will shape the future of semiconductor manufacturing. Here are four key takeaways.1. Monitor, Forecast, and PreventToday, tier 1 foundries use AI tools to combine equipment know-how and manufacturing statistics in managing massive Fault Detection (FD) data, much in the way that a car’s tire-pressure monitoring system helps maintain safe inflation levels and prevent accidents. For example, AI enables the real-time collection and monitoring of massive amounts of processing data, then alerts system administrators of any hardware failures or other manufacturing abnormalities.AI also makes it possible to adopt Run-to-Run (R2R) control to automate manufacturing process adjustments and corrections by providing feedback that can drive higher processing efficiency. In addition, virtual metrology replaces manual sampling inspection for comprehensive quality control, enabling foundries to improve yields, reduce costs, and strengthen their competitive advantage.2. Beyond Automation: Edge Computing The evolution of IoT is giving rise to a paradigm shift in the industry as the recognition grows that smart factories must go beyond automation to focus also on intelligence. All information – from equipment status and manufacturing process statistics to on-site environmental data – needs to be collected through sensors. In highly time-critical scenarios, returning all sensor data to the cloud for processing is time-consuming and impracticable. This is where edge computing’s real-time features and lower cost than cloud computing come into play.How does edge computing work in a smart factory? First, a rich trove of data from various devices is collected and integrated via Manufacturing Execution Systems (MES). Software analysis then produces a real-time factory production status before production data is visualized through a combination of system platforms and human-machine interfaces. In the end, the data is analyzed realtime in the cloud so failures can be predicted and prevented to help increase capacity and reduce costs. The approach is even capable of Bill of Materials (BOM) predictions, allowing better collaboration between upstream and downstream suppliers.3. Deep Learning Accelerates AI Deep learning enables autonomous driving, intelligent voice assistance and many other AI breakthroughs. The heart of deep learning is its ability to automatically process and learn data in various formats such as images, video and text with no human domain knowledge. This increases predictive accuracy and efficiency in processing massive amounts of data. Deep learning also enhances the efficiency of human-machine collaboration.4. Taiwan’s Competitive Niche: Industry 3.5Industry 4.0 is not just about improving production management. It also focuses on integrating supply chains, even among competitive companies. For Industry 4.0 to thrive, rival companies must grow together. The first and third industrial revolutions centered on disruptive technologies like steam engines, transistors and digital, while the second and fourth revolutions homed in on competition among various business models, platforms and industry ecosystems.While Taiwan’s strengths include innovation, short time-to-market, low manufacturing costs, and high supply chain management efficiency, the region still lags advanced countries in basic industry and research capabilities. Squeezed by Chinese supply chains and high-end manufacturers in advanced countries, Taiwan should start by carving out an Industry 3.5 niche for the island’s manufacturers. SEMI will continue to facilitate cross-industry connection, collaboration and innovation to help manufacturers seeking higher production efficiency and lower costs incorporate AI as a core competitive advantage. At SEMICON Taiwan 2018, SEMI will unveil its Smart Manufacturing Journey, an exhibition that gathers leading AI companies such as ABB, Advantech, Nvidia, Sony and UPS to demonstrate a comprehensive roadmap for smart manufacturing technologies and applications. For more information, please visit the SEMICON Taiwan website.Emmy Yi is a marketing specialist at SEMI Taiwan.