2 CROSS-SECTIONAL TECHNOLOGIES

State of the art

The increasing complexity of electronic embedded systems, hardware and software algorithms has a significant impact on the design of applications, engineering lifecycle and the ecosystems involved in the product and service development value chain.

The complexity is the result of the incorporation of hardware, software and connectivity into systems, and their design to process and exchange data and information without addressing the architectural aspects. As such, architectural aspects such as optimizing the use of resources, distributing the tasks, dynamically allocating the functions, providing interoperability, common interfaces and modular concepts that allow for scalability are typically not sufficiently considered. Today's complexity to achieve higher automation levels in vehicles and industrial systems is best viewed by the different challenges which need to be addressed when increasing the number of sensors and actuators offering a variety of modalities and higher resolutions. These sensors and actuators are complemented by ever more complex processing algorithms to handle the large volume of rich sensor data. The trend is reflected in the value of semiconductors across different vehicle types. While a conventional automobile contains roughly $330 value of semiconductor content, a hybrid electric vehicle with a full sensor platform can contain up to $1000 and 3,500 semiconductors. Over the past decade, the cost contribution for electronics in vehicles has increased from 18% to 20% to about 40% to 45%, according to Lam Research. The numbers will further increase with the introduction of autonomous, connected, and electric vehicles which make use of AI-based HW/SW components.

This approach necessitates the use of multiple high-performance computing systems to support the cognition functions. Moreover, the current Electrical and Electronic (E/E) architectures impose that the functional domains are spread over separated and dedicated Electronic Control Units (ECUs). This approach hampers upscaling of the automation functionality and obstructs effective reasoning and decision making.

Key focus areas

The major recommendations at the Embedded architectures infrastructure level are:

• Improving interoperability of systems: this is mainly covered by design methodology, where tools should be able to build a system from IPs coming from various sources. That means also that the description of the IPs, even if they are proprietary (black box), should contain all the view required to smoothly integrate them together. This is also a requirement for Open-Source Hardware. This can be extended at the level of integration in 2.5D systems based on interposers and chiplets: an ecosystem will only proliferate and flourish if a large catalogue of chiplets (in this case) are available and easily connected. As infrastructure for Embedded architectures, the “common platform” initiated by the European Processor Initiative (EPI) is an example of a template that allows to build different ICs with minimum efforts.
• Facilitating the easy addition of modules to a system: what is done at the Embedded architectures level can also be promoted at the system levels, where reuse of existing core could simplify the design, but perhaps at a cost of more complex software.
• Developing common interfaces and standards: this a basic element if we want to increase the productivity by reuse and the efficiency by using interoperability.
• Using AI techniques to help complexity management: existing Embedded architectures are so complex that humans cannot understand all the interactions and corner cases. Tools and techniques using Operational Research or Artificial Intelligence can be used to explore the space of conception and recommend optimum combinations and architectures. Automated Design Space Exploration is an emerging field, and AI is already used in backend tools by the major CAD tools providers (and by Google to design their TPUs).

The solutions and recommendations for Edge devices are similar of those for embedded computing:

• Improving interoperability of systems.
• Facilitating the easy addition of modules to a system.
• Developing common interfaces and standards, standardized APIs for hardware and software tool chains.
• Using AI techniques to help complexity management.

State of the art

To still achieve the required increased level of automation in automotive, transportation and manufacturing, disruptive frameworks are being considered offering a higher order of intelligence. Several initiatives to deliver hardware and software solutions for increased automation are ongoing. Companies like Renesas, NVIDIA, Intel/Mobileye, and NXP build platforms to enable Tier1s and OEMs to integrate and validate automated drive functions. Still, the “vertical” distribution of AI functionality is difficult to manage across the traditional OEM/Tier-1/Tier-2 value chain. Due to the long innovation cycle associated with this chain, vertically integrated companies such as Tesla/Waymo currently seem to hold an advantage in the space of autonomous driving. Closed AI component ecosystems represent a risk as transparency in decision making could prove hard to achieve and sensor level innovation may be stifled if interfaces are not standardized. Baidu (Apollo), Lyft, Voyage and Comma.ai take a different approach as they develop software platforms which are open and allow external partners to develop their own autonomous driving systems through on-vehicle and hardware platforms. Such open and collaborative approach might be the key to accelerate development and market adoption.

Next generation energy and resource efficient electronic components and systems that are connected, autonomous and interactive will require AI-enabled solutions that can simplify the complexity and implement functions such as self-configure to adapt the parameters and the resource usage based on context and real time requirements. The design of such components and systems will require a holistic design strategy based on new architectural concepts and optimized HW/SW platforms. Such architectures and platforms will need to be integrated into new design operational models that consider hardware, software, connectivity and sharing of information (1) upstream from external sources like sensors to fusion computing/decision processes, (2) downstream for virtualization of functions, actuation, software updates and new functions, and (3) mid-stream information used to improve the active user experience and functionalities.

Still, it is observed that the strategical backbone technologies to realize such new architectures are not available. These strategical backbone technologies include smart and scalable electronic, components and systems (controllers, sensors, and actuators), the AI accelerator hardware and software, the security engines, and the connectivity technologies. A holistic end-to-end approach is required to manage the increasing complexity of systems, to remain competitive and to continuously innovate the European electronic components and systems ecosystem. This end-to-end approach should provide new architecture concepts, HW/SW platforms that allow for the implementation of new design techniques, system engineering methods and leverage AI to drive efficiencies in the processes.

Based on the European's semiconductor expertise and in view of its strategic autonomy, we see an incentive for Europe to build an ecosystem on electronic components, connectivity, and software AI, especially when considering that the global innovation landscape is changing rapidly due to the growing importance of digitalization, intangible investment and the emergence of new countries and regions. As such, a holistic end-to-end AI technology development approach enables the advances in other industrial sectors by expanding the automation levels in vehicles and industrial systems while increasing the efficiency of power consumption, integration, modularity, scalability, and functional performance.

The new strategy should be anchored into a new bold digitalization transformation as digital firms perform better and are more dynamic: they have higher labor productivity, grow faster, and have better management practices.

The reference architectures for future AI-based systems need to provide modular and scalable solutions that support interoperability and interfaces among platforms that can exchange information and share computing resources to allow the functional evolution of the silicon-born embedded systems.

The evolution of the AI-based components and embedded systems is no longer expected to be linear and will depend on the efficiency and the features provided by AI-based algorithms, techniques and methods applied to solve specific problems. This allows to enhance the capabilities of the AI-based embedded systems using open architecture concepts to develop HW/SW platforms enabling continuous innovation instead of patching the existing designs with new features that ultimately will block the further development of specific components and systems.

Europe has an opportunity to develop and use open reference architecture concepts for accelerating the research and innovation of AI-based components and embedded systems at the edge, deep-edge and micro-edge that can be applied across industrial sectors. The use of reference open architecture will support the increase of stakeholder diversity and AI-based embedded systems, IoT/IIoT ecosystems. This will result in a positive impact on market adoption, system cost, quality, and innovation, and will support to ensure the development of interoperable and secure embedded systems supported by a strong European R&I&D ecosystem.

The major European semiconductor companies are already active and competitive in the domain of AI at the edge:

• Infineon is well positioned to fully realize AI’s potential in different tech domains. By adding AI to its sensors, e.g. utilizing its PSOC microcontrollers and its Modus toolbox, Infineon opens the doors to a range of application fields in edge computing and IoT. First, Predictive Maintenance: Infineon’s sensor-based condition monitoring makes IoT work. The solutions detect anomalies in heating, ventilation, and air conditioning (HVAC) equipment as well as motors, fans, drives, compressors, and refrigeration. They help to reduce breakdowns, maintenance costs and extend the lifetime of technical equipment. Second, Smart Homes and Buildings: Infineon’s solutions make buildings smart on all levels with AI-enabled technologies, e.g. building’s domains such as HVAC, lighting or access control become smarter with presence detection, air quality monitoring, default detection and many other use cases. Infineon’s portfolio of sensors, microcontrollers, actuators, and connectivity solutions enables buildings to collect meaningful data, create insights and take better decisions to optimize its operations according to its occupants’ needs. Third, Health and Wearables: the next generation health and wellness technology is enabled to utilize sophisticated AI at the edge and is empowered with sensor, compute, security, connectivity, and power management solutions, forming the basis for health-monitoring algorithms in lifestyle and medical wearable devices supplying highest precision sensing of altitude, location, vital signs, and sound while also enabling lowest power consumption.  Fourth, Automotive: AI is enabled for innovative areas such as eMobility, automated driving and vehicle motion. The latest microcontroller generation AURIX™ TC4x with the Parallel Processing Unit (PPU) provides affordable embedded AI and safety for the future connected, eco-friendly vehicle.

• NXP, a semiconductor manufacturer with strong European roots, has begun adding Al HW accelerators and enablement SW to several of their microprocessors and microcontrollers targeting the automotive, consumer, health, and industrial market. For automotive applications, embedded AI systems process data coming from the onboard cameras and other sensors to detect and track traffic signs, road users and other important cues. In the consumer space the rising demand for voice interfaces led to ultra-efficient implementations of keyword spotters, whereas in the health sector AI is used to efficiently process data in hearing aids and smartwatches. The industrial market calls for efficient AI implementations for visual inspection of goods, early onset fault detection in moving machinery and a wide range of customer specific applications. These diverse requirements are met by pairing custom accelerators, multipurpose and efficient CPUs with a flexible SW tooling to support engineers implementing their system solution.

• STMicroelectronics integrated Edge AI as one of the main pillars of its product strategy plan. By combining AI-ready features in its hardware products to a comprehensive ecosystem of software and tools, ST ambitions to overcome the uphill challenge of AI: opening technology access to all and for a broad range of applications. For the smart building domain, the STM32 microcontrollers embed optimized machine learning algorithms to determine room occupancy, count people in a corridor or automatically read water meters. The AI code compression is performed by users through the low-code STM32Cube.ai optimizer tool which enables a drastic reduction of the power consumption while maintaining the accuracy of the prediction. In Anomaly detection for industry 4.0, NanoEdge AI studio, an Auto-ML software for edge-AI, automatically finds and configure the best AI library for STM32 microcontroller or smart MEMS that contain ST’s embedded Intelligent Sensor Processing Unit (ISPU) while being able to do learning on device. It results in the early detection of arc-fault or technical equipment failure and extend the lifetime of industrial machines. Designers can now use NanoEdge AI Studio to distribute inference workloads across multiple devices including microcontrollers (MCUs) and sensors with ISPUs in their systems, significantly reducing application power consumption. Always-on sensors that contain the ISPU can perform event detection at very low power, only waking the MCU when the sensor detects anomalies.

Europe can drive the development of scalable and connected HW/SW AI-based platforms. Such platforms will efficiently share resources across platforms and optimize the computation based on the needs and functions. As such, the processing resource will dynamically adjust the type, speed and energy consumption of processing resource depending on the instantaneous required functionality.

This can be extended at the different layers of the architecture by providing scalable concepts for hardware, software, connectivity, AI algorithms (inference, learning) and the design of flexible heterogenous architectures that optimize the use of computing resources.

Optimizing the performance parameters of AI-based components, embedded systems within the envelope based on energy efficiency, cost, heat dissipation, size, weight using reference architecture that can scale across the information continuum from end point deep-edge to edge, cloud, and data centre.

Key focus areas

• Evolving the architecture, design and semiconductor technologies of AI-based components and systems, integration into IoT/IIoT semiconductor devices with applications in automation, mobility, intelligent connectivity, enabling seamless interactions and optimized decision-making for semi-autonomous and autonomous systems.
• New AI-based HW/SW architectures and platforms with increased dependability, optimized for increased energy efficiency, low cost, compactness and providing balanced mechanisms between performance and interoperability to support the integration into various applications across the industrial sectors.
• Edge, deep-edge and micro-edge components, architectures, and interoperability concepts for AI edge-based platforms for data tagging, training, deployment, and analysis. Use and development of standardized APIs for hardware and software tool chains.
• Deterministic behaviours, low latency and reliable communications are also important for other vertical applications, such as connected cars, where edge computing and AI represent “the” enabling technology, independently from the sustainability aspects. The evolution of 5G is strongly dependent on edge computing and multi-access edge computing (MEC) developments.
• Developing new design concepts for AI born embedded systems to facilitate trust by providing the dependable design techniques, that enable the end-to-end AI systems to be scalable, make correct decisions in repetitive manner, provide mechanisms to be transparent, explainable, interpretable, and able to achieve repeatable results and embed features for AI model’s and interfaces' interpretability.
• Distributed edge computing architecture with AI models running on distributed devices, servers, or gateways away from data centres or cloud servers.
• Scalable hardware agnostics AI models capable of delivering comparable performance on different computing platforms, (e.g., Intel, AMD or ARM architectures).
• Seamless and secure integration at HW/SW embedded systems with the AI models integrated in the SW/HW and APIs to support configurable data integrated with enterprise authentication technologies through standards-based methods.
• Development of AI based HW/SW for multi-tasking and provide techniques to adapt the trained model to produce close or expected outputs when provided with a different but related set of data. The new solutions must provide dynamic transfer learning, by assuring the transfer of training instance, feature representation, parameters, and relational knowledge from the existing trained AI model to a new one that addresses the new target task.
• HW/SW techniques and architectures for self-optimize, reconfiguration and to self-manage the resource demands (e.g. memory management, power consumption, model selection, hyperparameter tuning for automated machine learning scenarios, etc.).
• Edge-based robust energy efficient AI-based HW/SW for processing incomplete information with incomplete data, in real time.
• End-to-end AI architecture including the continuum of AI-based techniques, methods and interoperability across sensor-based system, device-connected system gateway-connected system, edge processing units, on-premises servers, etc.
• Developing tools and techniques helping in the management of complexity, e.g. using AI methods.

State of the art

Increasing lifetime of an electronic object is very complex and has multiple facets. It covers the life extension of the object itself up to the move of some of its critical parts in other objects and ultimately in the recycling of raw material in new objects. This domain of lifetime extension is very error prone as it is extremely easy to confuse some very different concepts such as upgradability, reuse up to recycling.

The first level of lifetime extension is clearly the upgrade to avoid replacing an object but instead improving its features and performance through either hardware or software update. This concept is not new as it is already applied in several industrial domains for dozens of years.

The second aspect of increasing lifetime is to reuse a system in an application framework less demanding in term of performance, power consumption, safety, etc.

Key focus areas

For re-using something in an environment for which it was not initially designed, it is key to be able to qualify the part in its new environment. To achieve this very challenging goal the main question is “what are the objective parameters to take into account to guarantee that the degraded part is compatible with its new working environment?”

• Intelligent reconfigurable concepts are an essential key technology for increasing the re-use and service life of hardware and software components. Such modular solutions on system level require the consideration of different quality or development stages of sensors, software, or AI solutions. If the resulting uncertainties (measurements, predictions, estimates by virtual sensors, etc.) are considered in networked control concepts, the interoperability of agents/objects of different generations can be designed in an optimal way.
• Distributed monitoring: continuous monitoring and diagnosis also play a crucial role for the optimization of product lifetime. Where a large amount of data is collected during daily life operation (e.g, usage, environment, sensor data), big data analysis techniques can be used to predictively manipulate the operational strategy, e.g. to extend service life. Similarly, an increase in power efficiency can be achieved by adjusting the calibration in individual agents. For example, consider a fuel cell electric vehicle where the operation strategy decisively determines durability and service life. Distributed monitoring collects data from various interconnected agents in real-time (e.g., a truck platoon, an aircraft swarm, a smart electricity distribution network, a fleet of electric vehicles) and uses these data to draw conclusions about the state of the overall system (e.g., the state of health or state of function). On the one hand, this allows to detect shifting behaviour or faulty conditions in the systems and to even isolate them by attributing causes to changes in individual agents in the network or even ageing of individual objects and components. Such detection should be accomplished by analysing the continuous data stream that is available in the network of agents. A statistical or model-based comparison of the individual objects with each other provides additional insights. Thus, for example, early failures of individual systems could be predicted in advance. This monitoring should also cover the performance of the semiconductor devices themselves, especially to characterize and adjust to aging and environmental effects and adjust operations accordingly.
• Another essential factor for increasing the lifespan of products is the intelligent use and handling of real-world data from products that are already in use and from previous generations of these. On the one hand, this allows for an optimal adaptation of the operating strategy to, for example, regionally, seasonally, or even individually varying use patterns. On the other hand, the monitoring of all agents (e.g. fleet of vehicles) also enables very precise estimates and predictions of certain conditions. This enables the detection of early failures of individual objects but also the timely implementation of countermeasures. Such approaches can be referred to as distributed monitoring.
• Distributed predictive optimization is possible, whenever information about future events in a complex system is available. Examples are load predictions in networked traffic control or demand forecasts smart energy supply networks. In automation, a concept dual to control is monitoring and state observation, leading to safety-aware and reconfigurable automation systems. Naturally, all these concepts, as they concern complex distributed systems must rely on the availability of vast data, which is commonly associated with the term big data. Note that in distributed systems the information content of big data is mostly processed, condensed, and evaluated locally thus relieving both communication and computational infrastructure.

State of the art

The novelty with AI systems is to upgrade while preserving and guaranteeing the same level of safety and performance. For previous systems based on conventional algorithmic approaches, the behavior of the system could be evaluated offline in validating the upgrade with a predefined data set representative enough of the operating conditions, knowing that more than the data themselves, the way they are processed is important. In the case of AI, things are completely different, as the way data are processed is not typically immediately understandable but what is key are the data set themselves and the results they produce. In these conditions it is important to have frameworks where people could reasonably validate their modification, whether it is hardware or software, in order to guarantee the adequate level of performance and safety, especially for systems which are human life critical. Another upgrade-related challenge is that of designing systems with a sufficient degree of architectural heterogeneity to cope with the performance demands of AI and machine learning algorithms, but at the same time flexible enough to adapt to the fast-moving constraints of AI algorithms. Whereas the design of a new Embedded architectures or electronic device, even of moderate complexity, takes typically 1-3 years, AI models such as Deep Neural Networks are outdated in just months by new networks. Often, new AI models employ different algorithmic strategies from older ones, outdating fixed-function hardware accelerators and necessitating the design of hardware whose functionality can be updated.

The other area of lifetime extension is how AI could identify very low signal in a noisy data environment. In the case of predictive maintenance for instance it is difficult for complex machinery to identify early in advance a potential failing part. More complex is the machinery and less possible is to have a complete analytic view of the system which would allow simulation and then identify in advance potential problems. Thanks to AI and collecting large dataset it is possible to extract some very complex patterns which could allow very early identification of parts with potential problem. AI could not only identify these parts but also give some advice regarding when an exchange is needed before failure, and then help in maintenance task planning.

Whatever the solution used to extend lifetime of systems, this cannot be achieved without a strong framework regarding standards and, even more important, for AI qualification framework of solutions. AI systems are new and show little standardization currently. Therefore, it is of high importance to devote effort to this aspect of AI-hardware and -software developments. Europe has a very diverse industrial structure, and this is a strength if all those players have early access to the standards frameworks for AI and its development vectors. Open access is therefore as important for the European AI ecosystem as the ability to upgrade and participate in the development of AI-interfaces. Another very important point is how we qualify an AI solution. Comparing to computing systems based on algorithm, where it exists a lot of tools and environment to detect and certify that a system has a given property thanks to static code analysis, formal proof, worst case execution time, etc. In case of AI, most of these solutions are not applicable as the performance of the system depends on the quality of datasets used for training and quality of data used during the inference phases.

For this reason, we suggest a strong and dedicated focus on upcoming AI-standards. Nevertheless, we need to keep in mind the strong business lever of standard and make sure that European companies will be able to build on top of standards and generate value at European level. For instance, android is open source but no way to make a competitive smartphone without a Google android license.

Interoperability, modularity, scalability, virtualization, upgradability is well known in embedded systems and are already widely applied. But they are brand new in AI and nearly non-existent in edge AI. On top, self-x (learning/training, configuration or reconfiguration, adaptation, etc.) are very promising but still under research or low level of development. Federative learning and prediction on the fly will certainly take a large place in the future edge AI systems where many similar equipment collect data (Smartphone, electrical vehicles, etc.) and could be improved and refreshed continuously.

One challenge of the AI edge model is upgradability of the firmware and the new learning/training algorithms for the edge devices. This includes the updates over-the-air and the device management of the updating of AI/ML algorithms based on the training and retraining of the networks (e.g., neural networks, etc.) that for IoT devices at the edge is very much distributed and is adapted to the various devices. The challenge of the AI, edge inference model, is to gather data for training to refine the inference model as there is no continuous feedback loop for providing this data. The related security questions regarding model confidentiality, data privacy etc. need to be addressed specifically for such fleets of devices.

At the application level, edge AI has a potential positive impact on ecologic sustainability: consider e.g., the application of AI to optimize and reduce the power consumption in manufacturing plants, buildings, households, etc. The potential impact is evident but, to ensure a real sustainable development and a real benefit, edge AI solutions will have to ensure that the costs savings are significantly larger than the costs required to design, implement, and train AI.

More generally, the implementation, deployment and management of large-scale solutions based on edge AI could be problematic and unsustainable, if proper engineering support, automation, integration platforms and remote management solutions will not be provided. At this level, the problem of sustainability includes business models, organizational aspects, companies’ strategies, partnerships, and it extends to the entire value chain proposing edge AI-based solutions.

Key focus areas

• Developing HW/SW architectures and hardware that support software upgradability and extension of software useful life. Secure software upgradability is necessary in nearly all systems now and hardware should be able to support future updates. AI introduces additional constraints compared to previous systems. Multiplicity of AI approaches (Machine learning, DL, semantic, symbolic, etc.), multiplicity of neural network architectures based on a huge diversity of neuron types (CNN, RNN, etc.), potential complete reconfiguration of neural networks for a same system (linked to a same use case) with a retraining phase based on an adapted set of data make upgradability much more complex. This this why HW/SW, related stacks, tools, data sets compatible with the Edge AI system must be developed in synergy. HW/SW plasticity is necessary whatever is the AI background principle of each system to make them as much as possible upgradable and interoperable and to extend the system lifetime. HW virtualization will help to achieve it as well as standardization. The key point is that lifespan extensions, like power management, are requirements which must be considered from day one of the design of the system. It is impossible to introduce them near the end without a strong rework.
• Standardization: standards are very difficult to define as they shouldn’t be too restrictive to avoid limitation to innovation but not too open also to avoid plenty of objects compliant to the standard but not interoperable because not supporting the same options of the same standard. For this reason, the concept of introducing standards early in the innovation process, must be complemented with a visionary perspective in view to expand the prospective standards for future expansions in function, feature, form, and performance.
• Re-use: One concept called the “2nd life” is actually the re-use parts of systems. Such re-use could be adapted to edge AI as far as some basic rules are followed. First, it is possible to extract the edge AI HW/SW module which is performing a set of functions. For example, this module performs classification for images, movements detection, sounds recognition, etc. Second, the edge AI module can be requalified and recertified downgrading its quality level. A module implemented in aeronautic systems could be reused in automotive or industrial applications. A module used in industrial could be reused in consumer applications. Third, an AI system may be re-trained to fit the “2nd life” similar use case, going for example from smart manufacturing to smart home. Last, business model will be affordable only if such “2nd life” use is on a significant volume scale. A specific edge AI embedded module integrated in tens of thousands of cars could be removed and transferred in a new consumer product being sold on the market.
• Prediction and improvements: prediction / improvements with pure analytics techniques are always difficult. Very often the analytic behaviours of some system parts are not known and then either approximate models are build-up, or it is just ignored. Thanks to AI, the system will be able to evolve based on data collected during its running phase. AI techniques will allow better prediction method based on real data allowing the creation of aggregated and more pertinent indicators not possible with pure analytic approach.
• Realizing self-X (adaptation, reconfiguration, etc.): for embedded systems self-adaptation, self-reconfiguration has an enormous potential in many applications. Usually in self-reorganizing systems the major issue is how to self-reorganize while preserving the key parameters of a system (performance, power consumption, real time constraints, etc.). For any system, there is an operating area which is defined in the multi-dimensional operating parameter space and coherent with the requirements. Of course, very often the real operating conditions are not always covering the whole operating domain for which the system was initially designed. Thanks to AI, when some malfunctioning parts are identified it could then be possible to decide, relying on AI and the data accumulated during system operation, if it affects the behaviours of the system regarding its real operating conditions. If it is not the case, it could be considered that the system can continue to work, with maybe some limitations, but which are not vital regarding normal operation. It would then extend its lifetime “in place”. The second case is to better understand the degraded part of a system and then its new operating space. This can be used to decide how it could be integrated in another application making sure that the new operating space of the new part is compatible with the operating requirements of the new hosting system.
• Self-learning techniques are promising. Prediction on Natural Language Understanding (NLU) on the fly or keyboard typing, predictive maintenance on mechanical systems (e.g., motors) are more and more studied. Many domains can benefit of the AI in mobility, smart building, communication infrastructure.
• Dynamic reconfiguration: a critical feature of the AI circuits is to dynamically change their functions in real-time to match the computing needs of the software, AI algorithms and the data available and create software-defined AI circuits and virtualize AI functions on different computing platforms. The use of reconfigurable computing technology for IoT devices with AI capabilities allows hardware architecture and functions to change with software providing scalability, flexibility, high performance, and low power consumption for the hardware. The reconfigurable computing architectures, integrated into AI-based circuits can support several AI algorithms (e.g., convolutional neural network (CNN), fully connected neural network, recursive neural network (RNN), etc.) and increase the accuracy, performance and energy efficiency of the algorithms that are integrated as part of software define functions.
• From the engineering perspective, leveraging open source will help developing European advanced solutions for edge AI (open-source hardware, software, training datasets, open standards, etc.).

As a summary, intelligence at the edge sustainable engineering will have to face many challenges:

• Supply chain integrity for development capability, development tools, production, and software ecosystems, with support for the entire lifecycle of edge AI based solutions.
• Security for AI systems by design, oriented also to certify edge AI based solutions. European regulations and certification processes would lead to a global compelling advantage.
• Europe needs to establish and maintain a complete R&D ecosystem around AI.
• Europe need to address the end-to-end value chain and supports its SMEs.
• Identification of a roadmap for standardization that does not hinder innovation: the right balance that ensure European leadership in edge AI.
• Europe must strive for driving a leading and vibrant ecosystem for AI, with respect to R&D, development and production, security mechanisms, certifications, and standards.

State of the art

One of the major challenges that need to be accounted for in the next few years is related to the design of progressively more complex electronic systems to support advanced functionalities such as AI and cognitive functionality. This is particularly challenging in the European landscape, which is dominated by small and medium enterprises (SMEs) with only some large actors that can fund and support larger-scale projects. To ensure European competitiveness and sustainability in advanced Embedded architectures it is therefore crucial to create an ecosystem, and the means, in which SMEs can cooperate and increase their level of innovation and productivity. This ecosystem needs to cover at the best all part of the value chain from concept to design till production. The definition of open industrial standards and a market of Intellectual Properties (IPs) are required to accelerate the design, competitivity and create a larger market. Open source on Software, Hardware and tools can play an extremely important role in this regard. Open-source solutions significantly allow to reduce engineering costs for licensing and verification, lowering the entry barrier to design innovative products.

Key focus areas

• Energy efficiency improvement:
  • New materials, new embedded non-volatile memories with high density and ultra-low power consumption, substrates and electronic components oriented to low and ultra-low power solutions.
  • 3D-based device scaling for low power consumption and high level of integration.
  • Strategies for self-powering nodes/systems on the edge.
  • Low and ultra-low power and interoperable communications components.
  • Efficient cooling solutions.
• Improving sustainability edge computing:
  • Efficient and secure code mobility.
  • Open edge computing platforms, providing remote monitoring and control, security, and privacy protection.
  • Solutions for the inclusion/integration of existing embedded computers on the edge.
  • Policies and operational algorithms for power consumption at edge computing level.
  • New benchmarking approach considering sustainability.
• Leveraging open source to help developing European advanced solutions on the edge:
  • Open-source hardware (and its complete ecosystem of Ips and tools).
  • Open-source software.
  • Europe must address the end-to-end value chain.
• Engineering support to improve sustainable edge computing:
  • Engineering process automation for full lifecycle support.
  • Edge devices security by design.
  • Engineering support for edge computing, verification, and certification, addressing end-to-end edge solutions.

State of the art

First, as Embedded Artificial Intelligence is developing quickly and in many different directions for new solutions, it is crucial that a European ecosystem emerge gathering all steps of the value chain. It has then to include the hardware, the software, the tools chain for AI development and the data sets in a trustable and certifiable environment. Both Edge Computing and Embedded Artificial Intelligence ecosystems are tied together.

Next, technology is strongly affected by sustainability that, very often, tips the scale between the ones that are promising, but not practically usable, and the ones making the difference. e.g. cloud computing, based on data centres, plays a fundamental element for the digitalization process. However, data centres consume a lot of resources (energy135, water, etc.) and they are responsible for significant carbon emissions, during their entire lifecycle, and generate a lot of electronic and chemical waste.

Today, the percentage of worldwide electricity consumed by data centres is estimated to exceed the 3%, while the CO2 emissions are estimated to reach the 2% of worldwide emissions136 137, with cloud computing that is responsible for half of these emissions. A recent study predicts that, without energy efficient solutions, by 2025 eight data centres will consume 20% percent of the world’s energy, with a carbon footprint rising to 5.5% of the global emissions. Data centres are progressively becoming more efficient, but shifting the computing on the edge, for example, allows to temporally reduce data traffic, data centres storage and processing. However, only a new computing paradigm could significantly reduce their environmental footprint and ensure sustainability. Edge Computing could contribute to reach this goal by the introduction of ultra-low and efficient computing solutions.

Indeed, from a wider perspective, digital transformation relies largely on other technologies that could significantly impact sustainability, including edge and fog computing, AI, IoT hyper connectivity, etc. In recent years, artificial intelligence and cloud computing have been the focus of the scientific community, environmental entities, and public opinion for the increasing levels of energy consumption, questioning the sustainability of these technologies and, indirectly, their impact on corporate, vertical applications and societal sustainability. For example, devices are already producing enormous amounts of data and a recent study138 estimates that by 2025 communications will consume 20% of all the world’s electricity. This situation has been worsening with COVID-19 pandemic that generated a worldwide reduction of power consumption because of global lockdown restrictions but, at the same time, caused a huge spike in Internet usage: NETSCOUT measured an increase of 25-35% of worldwide Internet traffic in March 2020, just due to remote work, online learning and entertainment. This spike in Internet use provides a flavor of the implications of digitalization on sustainability. Reducing energy of computing and storage devices is a major challenge (see Major Challenge 1 on “Increasing the energy efficiency of computing systems”).

Shifting to green energy is certainly a complementary approach to ensure sustainability, but the conjunction of AI and edge computing, the Edge AI, has the potential to provide sustainable solutions with a wider and more consolidated impact. Indeed, a more effective and longer-term approach to sustainable digitalization implies reconsidering the current models adopted for data storage, filtering, analysis, processing, and communication. By embracing edge computing, for example, it is possible to significantly reduce the amount of useless and wasteful data flowing to and from the cloud and data centres, with an architectural and structural more efficient solutions that permanently reduces the overall power consumption and bring other important benefits such as real-time data analysis reducing the amount of data to be stored and then a better data protection. The Edge Computing paradigm also makes AI more sustainable: it is evident that cloud-based machine learning inference is characterized by a huge network load, with a serious impact on power consumption and huge costs for organizations. Transferring machine learning inference and data pruning to the edge, for example, could exponentially decrease the digitization costs and enable sustainable businesses. To avoid this type of drawbacks, new AI components should be developed based on neuromorphic architectures and considering the application areas, in some cases, this could bring to a more specialised and very efficient solutions.

Sustainability of Edge Computing and AI is affected by many technological factors, on which Europe should invest, and, at the same time, they have a positive impact on the sustainability of future digitalization solutions and related applications.

GAMAM already master these technologies and are progressively controlling the complete value chain associated with them. To follow this trend and aim at strategic autonomy, Europe has therefore to fill the technology gaps and address the value chain end to end, with a particular attention to SMEs (which generate a large part of European revenues) and leveraging on the cooperation between the European stakeholders in the value chain to develop successful products and solutions. From this perspective, European coordination to develop AI, edge computing and edge AI technologies is fundamental to create a sustainable value chain based on alliances and capable to support the European key vertical applications.

It will be a challenge for Europe to be in this race, but the emergence of AI at the edge, and its know-how in embedded systems, might be winning factors. However, the competition is fierce, and the big names are in with big budgets and Europe must act quickly, because US and Chinese companies are already also moving in this "intelligence at the edge" direction.

Key focus areas

On top of the key focus area for Edge computing, Embedded Artificial Intelligence also requires:

• Energy-efficiency improvement:
  • New memories used to mimic synapses.
  • Advanced Neuromorphic components.
• Improving sustainability of AI:
  • Re-use and share of knowledge and models generated by embedded intelligence.
  • Energy- and cost-efficient AI training.
  • New benchmarking AI approach considering sustainability.
• Leveraging open source to help developing European AI advanced solutions on the edge:
  • Open-source training datasets.
  • Open Frameworks including AI tools.
  • Europe must address the end-to-end Embedded Intelligence value chain.
• Engineering support to improve sustainable AI:
  • Edge AI security by design.
  • Engineering support for AI verification and certification.
  • Education and support to deploy Edge AI.