In the context of the integration of technologies such as the Internet of Things, 5G, AI, and big data, smart edge computing has rapidly transitioned from concept to reality. Especially since the outbreak of the COVID-19 pandemic, an increasing number of network applications, such as video surveillance, smart retail, smart manufacturing, and smart cities, have developed rapidly, and more data is being stored and processed at the edge. According to Gartner, it is predicted that by 2025, 75% of data will be generated outside data centers at the edge, and digital transformation is shifting from the cloud to networks and endpoints. However, the smart edge also presents more new challenges for industry enterprises. How will technology trends develop? How can an end-to-end digital infrastructure be built? How can related applications be implemented to empower industries such as healthcare, agriculture, smart transportation, and retail?
The Fusion of Intelligence and Data: Edge Computing Takes Center Stage
The Internet of Things is not a new concept. If we consider the proposal of the “Perception China” concept in 2009 as the landmark moment for the introduction of the IoT concept in China, it has been 13 years. The concept of edge computing was officially proposed in 2013, and it has also been nearly 10 years. However, with the further deepening of the digital economy, people have raised higher requirements for networks and computing power, and the trend of mutual integration between IoT and edge computing has become increasingly evident, making the concept of smart edge a new industry hotspot.
According to Zhang Yu, Global Chief Technology Officer of Intel’s Video Division, the reason why smart edge computing is receiving increasing attention is that society is undergoing a profound digital transformation. In this process, the operations of various industries must rely on a complete set of end-to-end digital infrastructure to function normally. The edge is an organic part of the entire system, and its role is becoming increasingly prominent.
“The emphasis on ‘end-to-end’ is because the services that this system needs to provide are all end-to-end services, from data collection at the front end to data transmission, and then to data processing, storage, and feedback at key nodes. The entire service is a complete end-to-end process, and only such a complete process can be effectively accomplished,” Zhang Yu pointed out. In such a data transmission and processing process, some data can be completed at the central node, while others must be processed at the edge node. This is determined by the different demands of data applications. Applications that do not require real-time processing can transmit data from the application front end to a remote cloud, where there is more storage space and relatively abundant computing power, allowing for more cost-effective computation and storage. However, for applications such as autonomous driving and industrial internet that require high real-time performance, it is inappropriate to transmit data to a remote location for processing. This kind of computation often needs to be supported by edge computing architectures like computing networks. This is the main reason why smart edge computing is receiving increasing attention.
“Without the intelligence of IoT and the ubiquity of computing networks, it is impossible to support the real-time and efficient computational demands of applications at the front end,” Zhang Yu emphasized.
For smart edge computing, there are three future development trends. First, the construction of digital infrastructure will be a collaborative and integrated system of “end-edge-cloud.” None of the three can be absent, and their roles vary depending on the application scenario, with some data processing being more critical at the edge, while in other cases, the cloud plays a more important role.
Secondly, the role of software in digital transformation cannot be ignored. This is often referred to as “software-defined.” With the development of remote work, the demand for network flexibility is constantly increasing. This requires having a network that can be flexibly deployed, not just the core network but also the configuration of data centers, which must become increasingly flexible. Data centers need to build network infrastructure in a programmable way, with programmable Ethernet chips and programming languages becoming important supports for data center construction.
Thirdly, the penetration of artificial intelligence in edge computing is becoming increasingly widespread. Although the current wave of AI application has its origins in data centers, and a large amount of data processing is completed in the cloud during the initial phase, with the development of technology, more and more data processing is shifting to the edge. The mutual penetration and integration of edge computing and artificial intelligence is an inevitable trend.
In summary, against the backdrop of the integration of technologies such as the Internet of Things, 5G, AI, and big data, smart edge computing is rapidly transitioning from concept to reality. According to IDC data, by 2025, approximately 75% of data will be generated and processed at the edge. The increasing volume of data generated also continuously raises the requirements for edge computing power. The smart edge will become an important computing domain for end-to-end digital infrastructure in the digital age.
Hardware-Software Integration Supports the Ubiquitous Development of Computing Networks
In the rapid development of smart edge computing, chips play a key role. “In fact, innovative applications of smart IoT can only be realized through underlying chips and supporting software. From the perspective of chips, as the demand for computing power grows, the requirements for chips will also increase,” Zhang Yu pointed out.
Intel has long been deeply engaged in the IoT industry, providing a diverse product line to meet user needs. At this year’s International Consumer Electronics Show (CES 2022), Intel released the 12th generation Core processors (codenamed Alder Lake S series and H series). This is Intel’s first processor series optimized for the edge, integrating high-performance performance cores with efficiency cores and hardware thread schedulers (Thread Director) to optimize the acceleration of IoT application innovation, suitable for users in fields like retail, manufacturing, healthcare, and video customers for graphics, multimedia, display, and AI computations.
Zhang Yu also emphasized that while Intel is committed to improving chip product performance, it also places great importance on achieving sustainable development and green computing concepts. “We cannot just emphasize the enhancement of computing power; we must also recognize the importance of improving efficiency ratios. Intel has always focused on promoting the orderly development of society in a sustainable manner.”
It is reported that from 2010 to 2020, the energy efficiency ratio of Intel’s Core product line improved by 14 times. From 2020 to 2030, Intel aims to further increase the average energy efficiency ratio of its product line by 10 times based on existing levels. To this end, Intel has introduced the concept of “big and small cores” in the 12th generation Core processors, including performance cores and efficiency cores, using different cores to match computational demands and achieve optimal load to reduce overall power consumption.
Heterogeneous computing is also an important technological direction for addressing differentiated demands and improving processor energy efficiency ratios. Many loads are typical, whether in video conferencing scenarios, smart retail scenarios, or cloud gaming scenarios, the underlying technology involves video encoding, decoding, and transcoding. For these relatively fixed loads, processing with proprietary hardware is more efficient and consumes less power. Therefore, in the Core product line, Intel integrated integrated graphics early on to form a hardware unit dedicated to video encoding and decoding. Of course, for users with higher performance requirements, Intel can also provide solutions with discrete graphics cards. The overall idea is to improve energy efficiency ratios through a heterogeneous architecture.
How to utilize these hardware products depends on software support. Zhang Yu emphasized that a good software tool can allow developers to fully leverage hardware performance. Intel has been working with partners in this area, including the launch of oneAPI to achieve access to heterogeneous hardware resources through the underlying DPC++ open interface. For developers, using oneAPI allows for unified programming across different hardware.
Intel has also launched the OpenVINO toolkit to accelerate AI inference at the edge. OpenVINO enables developers to train in different AI frameworks. In some AI model scenarios, OpenVINO can provide up to 7 times inference acceleration, effectively enhancing work efficiency and increasing platform competitiveness.
Industry + Retail: Edge Intelligence Accelerates Implementation
As smart edge computing develops, its successful applications in certain areas are becoming evident.
Industrial manufacturing is one of the key areas where smart edge computing is penetrating and developing. According to a recent research report released by MarketsandMarkets, the global smart manufacturing market size was $88.7 billion in 2021 and is expected to reach $228.2 billion by 2027, with a compound annual growth rate of 18.5%. In this regard, Zhang Yu stated: “Machine vision, as an important technology for the intelligent transformation of manufacturing, is evolving rapidly with the high-speed development of artificial intelligence, and its market size is growing rapidly. In fact, the current wave of artificial intelligence explosion is based on deep learning, which relies on image recognition. Currently, about 80% of the data collected in the IoT field is related to graphics and images.”
Based on this, Intel recently partnered with Xunbu Technology to launch a series of machine vision development kits. The development kits are framed by the Industrial Edge Insight Platform (EII) and visual software optimization packages, integrating software tools and libraries such as OpenVINO, DPC++/C++ compiler, oneAPI Math Kernel Library (oneMKL), Vtune™ Profiler, IPP, OpenMP, and TBB, pre-setting reference cases and algorithm optimization methods for typical industrial application scenarios, providing users with an end-to-end software framework for developing machine vision applications. Currently, the development kits have been successfully implemented in industries such as automotive manufacturing, 3C/semiconductors, food packaging, and logistics warehousing, effectively assisting the manufacturing industry in evolving towards intelligence and informatization.
The competition in the retail market is becoming increasingly fierce, and the significant value demonstrated by artificial intelligence is gaining widespread attention from global retailers. Through the application of artificial intelligence technology, retailers can better understand consumer preferences, provide personalized and unique services, and enhance the attractiveness of retail services. At the same time, artificial intelligence also helps retailers automate more processes, achieving greater profits. Data shows that by 2035, artificial intelligence will increase profit margins in retail and wholesale businesses by nearly 60%.
Faced with this emerging application market, Intel, in collaboration with Hanshuo Technology and Microsoft, has jointly launched smart edge solutions for the retail industry, primarily applied in smart shelf management and self-checkout loss prevention. “People, goods, and places” are the main links in traditional retail, and this solution integrates Intel’s full-stack technology from software to hardware, helping retail customers build a high-performance and easily manageable smart retail management system.
In addition, edge intelligence has broad application potential in smart healthcare, smart transportation, and accelerating digital transformation for enterprises. “The development space for artificial intelligence in edge networks is enormous, and market demand is very clear. In the past, people always thought that artificial intelligence was something very advanced, with high thresholds and difficult to deploy. However, the reality is that more and more companies have begun to deploy and operate artificial intelligence technology successfully. This situation precisely illustrates that the threshold for using artificial intelligence is lowering, and it is also a typical example of the ubiquity of artificial intelligence,” Zhang Yu pointed out. In this process, Intel’s technologies, including the OpenVINO toolkit and a series of hardware products, also play a key role.
Localization Adaptation: Breaking Through the “Last Mile”
In the accelerated implementation of smart edge computing, how to better adapt it to local needs and break through the bottleneck of the “last mile” is also a very critical aspect. In an interview, Zhang Yu specifically pointed out: “The way technology is implemented in data centers and at the edge is different.” After all, platforms are different, computing power is different, and conditions are different, so the implementation methods will not be the same. This means that when artificial intelligence is implemented at the edge, specific problems that only arise at the edge must be solved well.
For example, artificial intelligence is generally divided into two stages: the training stage and the inference stage. The training stage requires the use of a large amount of data for training, and prior to training, data needs to be annotated, marking the objects of interest in the data and then transmitting the annotated images to the training platform for training, ultimately generating an AI network model that can perform inference.
However, this model may face challenges at the edge. In reality, the edge often does not have as much data available for training. For example, defect detection is one of the most common applications of AI in smart factories. However, the probability of defects occurring on a normal production line is not that high. This means that the sample data available for training is relatively limited. How to train a usable model under small sample conditions is a problem that smart edge computing needs to solve.
Moreover, during training, annotation is required, but in edge applications, the personnel operating the AI are often the production staff on the production line. These individuals find it challenging to have the extra energy to perform this annotation work. Therefore, developing automated annotation tools to reduce the burden on operators and ensure that the closed loop from training, annotation to inference can operate effectively is also a problem that Intel needs to solve when implementing AI at the edge.
“In fact, there are many problems at the edge that need to be solved in a specific manner,” Zhang Yu emphasized.
Building an Industrial Ecosystem: Addressing Fragmentation Challenges
Of course, in the field of smart edge, the completion of all these tasks by Intel relies on the support of partners. “Intel has long been deeply engaged in the IoT industry, providing a rich variety of product types. We hope that through these different combinations of products, partners can have more room to play in the IoT field,” Zhang Yu pointed out.
When discussing Intel’s competitive advantage in the smart edge field, Zhang Yu attributed it to having a broad partner base. “This allows us to better understand user demands and formulate software and hardware solutions based on those demands. Whether these users are at the network front end, edge, or data center, Intel can provide end-to-end solutions that align with the development trends of digital infrastructure.”
Although the Internet of Things holds infinite possibilities, the problem of fragmentation remains a significant challenge for vendors. In this regard, Zhang Yu stated that on one hand, we need to ensure our products are as universal as possible; but on the other hand, project implementation cannot be separated from close cooperation across the entire industry chain.
Different markets have different demands. The characteristic of the Chinese IoT market is its rapid development speed, with many innovative customers. These customers may not be large in scale but possess strong innovation capabilities. In recent years, Intel has worked extensively with domestic partners, including system integrators, to integrate product technologies across different links of the industry chain to address fragmentation issues.
In 2016, Intel, along with partners, established the Edge Computing Industry Alliance, which is currently the largest alliance in China focused on edge computing, with over 300 member units, covering chip manufacturers, system integrators, software developers, ODMs, OEMs, and other vendors. Through this alliance platform, we explore solutions for IoT and edge computing and industry solutions, collaboratively addressing fragmentation issues.
“Water nurtures everything without contention” may be the best description of Intel’s positioning in the entire ecosystem. Over the years, through quiet and diligent work, Intel has connected various links of the edge intelligence industry chain, including original design manufacturers (ODMs), original equipment manufacturers (OEMs), system integrators (SIs), independent software vendors (ISVs), etc., providing the industry with an end-to-end solution aimed at digital infrastructure.
Intel CEO Pat Gelsinger proposed the concept of four super technological forces at this year’s “2022 Intel On Industry Innovation Summit,” including ubiquitous computing, ubiquitous connectivity, artificial intelligence, and infrastructure from cloud to edge. Zhang Yu emphasized that these four super technological forces are actually the four pillars needed to build a green, intelligent, end-to-end digital infrastructure in the future. If these four super technological forces are used comprehensively, what will be built is an end-to-end digital infrastructure. In the future, this digital infrastructure will support more new creations and applications. These infrastructures have two characteristics: one is intelligence, and the other is green.
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