Enabling the smart edge

Enabling the smart edge

Background and scope

The concept of the smart edge encompasses a wide range of devices situated in or near the location where data is acquired or generated. As data processing moves closer to the edge of the network, a new generation of smart edge devices is emerging, requiring innovative solutions for low-power processing, sensing, and communication.

The concept of smart edge recognises the limitations and challenges of relying solely on centralized cloud-based processing. By bringing intelligence closer to the data source, smart edge offers several advantages for instance with real-time processing at the edge, there is a significant reduction in latency, which is crucial for applications that require immediate responses and actions. Other advantages include bandwidth optimization that is particularly important in scenarios where network connectivity is limited or expensive, enhanced privacy and security by keeping sensitive data locally and reducing the exposure of data during transmission, as well as real-time decision-making without relying on cloud connectivity or remote servers.

The potential market size for smart edge solutions is expected to be significant, driven by the increasing adoption of edge computing, IoT, and AI technologies across various industries,, with an expected growth rate between 30% and 40% until 2023, according to most market analysts.[[McKinsey, Mordor Intelligence, Grand View Research, Fortune BI, others.]]

Specific objectives

The objective of this Challenge is to promote the development of novel semiconductor components and integrated smart systems for next-generation edge devices with significant impact. The proposals should focus on development of smart integrated devices where the competitive advantage may lie in the system approach or in one of the key components or technologies, such as the following:

• Edge Processing – involving the design and/or integration of edge processors that minimize energy consumption and enable real-time decisions: low- and ultralow-power processors, open-source processor cores, embedded System-on-Chip processors, programmable processors (e.g.,FPGAs) AI accelerators, and neuromorphic processors. Processors will require low-latency non-volatile memory for local data storage; some NV-RAMs allow for highly efficient in-memory computing and analog computing. Security is another critical aspect and may involve cryptographic accelerators and hardware security modules.
• Edge Sensing and Imaging- including the design and/or integration of components for data acquisition: optical sensors, Lidars, Radars, T-o-F sensing, biometric sensing, environmental sensing, chemical and gas sensing, and MEMS.
• Edge Communication - covering the design and/or integration of connectivity and communication technologies on chips for edge devices: 5G and 6G wireless communication, low-power wireless communication, optical connectivity, mesh networking, software-defined networking, and security protocols for edge and IoT applications.
• Edge Power Management - involving the design and/or integration of components to efficiently manage and utilise power, such as those based on wide bandgap materials. This includes solutions for dynamic power management, sleep mode optimization, battery optimization, and energy harvesting for sustainable and autonomous operation.
• Integrated Smart Edge Devices - referring to highly integrated customised edge devices based on System-on-Chip integration, System-in-Package integration, heterogeneous integration, and modular design of components, such as chiplets, for integration into customized edge devices through advanced packaging technologies, including 2.5D and 3D packaging, enabling improvements in device miniaturisation, performance and reliability.

Relevant examples of the use of integrated chips in edge devices include smart cameras, wearables, hearing aids, AR/VR gear, industrial automation devices, drones, as well as network edge nodes, 5G/6G base stations, and autonomous vehicles. Proposals should demonstrate high potential for commercial deployment in key EU industry sectors such as industrial automation, information and communication, mobility, health and well-being, agri-food, security, and energy.

Expected outcomes and impacts

This Challenge should lead to deep-tech innovations for next-generation edge and IoT semiconductor ships devices that will have important impact for the smart edge, including:

• Industrial Automation: enabling real-time monitoring of machinery, predictive maintenance, and automated decision-making to increase productivity, reduce downtime, and improve safety in industrial settings.
• Mobility: enabling intelligent transportation systems and new mobility services and models, (e.g.,automated vehicles) significantly improving efficiency, effectiveness, safety, and sustainability.
• Smart Cities: enabling real-time monitoring of traffic, energy usage, air quality, leading to reduced congestion, improved sustainability, and enhanced quality of life for city residents.
• Health and Well-being: enable remote patient monitoring, personalized treatment plans, and real-time analysis of medical data to improve patient outcomes, reduce healthcare costs, and increase access to care.
• Agriculture: more efficient and sustainable by enabling precision farming techniques to increased crop yields, reduced water usage, and enhanced environmental sustainability.
• Environmental Monitoring: to improve resource management, early warning systems for natural disasters, and enhanced environmental sustainability.

Specific conditions

In case of an investment support, specific safeguards may be introduced in the investment agreement (see Introduction, section on Economic Security).]