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Advances in AI, edge computing and IoT offer a new playground for data processing. By harnessing information precisely where and when it’s needed, Edge AI meets the demand for instantaneity and energy frugality. Edge AI-based systems analyze and make decisions faster, enhance data privacy and security, consume less energy and improve the user experience: major assets in all sectors. In cities, industry and transport, information processing is becoming intelligent and instantaneous.
Edge AI refers to the integration of artificial intelligence in an edge computing environment. This approach involves harnessing and storing data from sensors or connected objects inside or close to the device in use, for real-time decision-making without connection to the cloud or remote data centers.
Data processing at the edge is the major feature of Edge AI, from which derive the main advantages presented later in this article. It overcomes the recognized weaknesses of cloud computing: high latency, limited bandwidth and concerns about data confidentiality. The technology is also reliable, energy-efficient and cost-effective.
Transferring data between the cloud or data center and the operating machine takes time and generates latency problems. Edge AI overcomes this obstacle by processing and storing all data directly on the device, connecting to the Internet only when necessary or desirable. This significantly reduces latency, which is crucial for applications requiring instant responsiveness, such as industrial automation, autonomous driving, surveillance systems or certain medical devices.
Because information is processed locally, Edge AI relieves network congestion by reducing the amount of data to be transmitted for analysis. This optimizes bandwidth utilization and minimizes latency problems.
By running artificial intelligence algorithms directly on the device, such as continuous learning or federated learning, Edge AI limits the transmission of sensitive data to the cloud, reinforcing confidentiality and privacy. Data is stored on the device or in a local cloud controlled by the user, minimizing the risk of interception or hacking when transmitted over external networks. Even if some data is sent back to the cloud for learning purposes, it can be anonymized to preserve user identity. It is essential, however, to put in place rigorous security measures to ensure that locally processed information remains protected against any attempt at unauthorized access.
Unlike cloud-based solutions, Edge AI-based devices are able to operate without a permanent connection to a dedicated network or the Internet. Their autonomy from network fluctuations and connectivity issues increases their reliability, ensuring uninterrupted operation. AI capabilities now extend to previously inaccessible areas, where Internet connectivity is absent or unreliable.
By deploying computing resources at the edge, data processing and task execution processes consume less energy. However, techniques such as model quantization are needed to reduce energy consumption on resource-constrained systems. It may also be useful to integrate standby modes with reactivation on demand. In this case, energy consumption can be virtually zero when a device is not in use, considerably extending its autonomy without the need for recharging or a new battery. This feature is particularly advantageous for remote video cameras, medical implants and embedded sensors.
Edge AI reduces dependence on expensive cloud infrastructures and unstable network connections, cutting processing and data transfer costs. What’s more, by performing real-time analysis and automated decisions directly on connected devices, Edge AI increases operational efficiency, limits downtime and optimizes processes, resulting in more efficient use of resources and increased productivity.
By equipping vehicles with local processing and decision-making capabilities, Edge AI marks a new era in the automotive industry. On-board AI algorithms can analyze data from in-car sensors, such as cameras, LiDARs and radars, to detect and react to obstacles, road signs, pedestrians and other vehicles on the road. This analysis can be carried out continuously, even in areas without connectivity, such as tunnels or remote regions. In addition, Edge AI improves the responsiveness of ADAS systems, such as automatic emergency braking or lane keeping, helping to reduce road accidents and increase driver and passenger safety.
Edge AI can also be used to automatically manage urban traffic via data collected from intelligent sensors placed at traffic lights. Each sensor detects the movements of cars, cyclists and pedestrians, and makes decisions accordingly. This provides a better understanding of the traffic flow and enables the timing of traffic lights to be adapted accordingly.
To identify the right tools and technologies for their use case, companies need to define their requirements in terms of confidentiality, data security and latency, while taking into account available IT resources and network interconnection.
Implementing an efficient, reliable network edge operational system that matches the use case involves designing an ideal software-hardware combination. This calls on a range of knowledge and skills in the following fields:
Building on its expertise in Computer Vision, IMPULSE, LACROIX’s integrated engineering office, develops AI software and algorithmic solutions for embedded vision systems. We work with you, taking into account your constraints in terms of latency, data volumes to be processed and costs, from design and implementation through to deployment of updates, to guarantee reliable, secure long-term operation.
Work with the experts at IMPULSE to integrate Edge AI into your project.