While artificial intelligence is often associated with cloud computing, more and more AI workloads are migrating to local devices - a phenomenon known as edge AI. This article explores the advantages of edge AI and how Clea integration enables its implementation.
While artificial intelligence is often associated with cloud computing, more and more AI workloads are migrating to local devices—a phenomenon known as edge AI. Thanks to the emergence of low-cost AI-enabled embedded processors and lightweight AI models, you can now find AI running on machinery on the factory floor, inside medical equipment, and across many other edge applications.
The advantages of edge AI implementation are substantial. The biggest benefit is reduced latency. Instead of waiting for a response from a far-away data center, and incurring nondeterministic network communication times, data can be processed in real time. Keeping sensitive data on the local device also improves security, dramatically reducing the opportunities for interference with AI-guided decision-making. These factors enables AI to power reliable real-time operation—a key feature for time-critical use cases like autonomous vehicles and robotics that may not be feasible within a cloud-based AI paradigm.
Edge AI also reduces energy consumption by eliminating the need for constant data transfers to the cloud—a crucial feature for edge devices running on limited power. Companies also save money by processing data locally instead of paying for continuous cloud bandwidth.
Perhaps most importantly, edge AI systems continue working even when internet connections fail, providing reliability that cloud-dependent systems cannot match.
Need for Multi-layered Intelligence
Although edge AI has many advantages, it also has its limitations. Many systems need multi-layered intelligence that combines edge, cloud, and fog computing to balance efficiency, speed, and scalability. To understand why, let’s consider a typical video surveillance system:
- Edge AI processes data right on the end device—for example, inside an intelligent surveillance camera.
- Fog computing adds a layer between the edge and the cloud that can handle data from multiple sources. For example, a local network video recorder (NVR) system connected to several cameras can track objects moving between different camera views and communicate derived results to the cloud as needed.
- Cloud computing aggregated and processes large amounts of data in data centers. This works well for long-term storage, training AI models, and analyzing data over time—such as tracking security trends across an entire city.
By combining these approaches, an edge-to-cloud system or an edge-to-fog-to-cloud system can deliver greater value than a stand-alone point solution.
Clea Covers All Components of the Value Chain
Recognizing the importance of a holistic approach, SECO provides both hardware and software solutions that span edge, fog, and cloud AI.
Edge AI hardware solutions span a wide variety of standard form-factor computing modules and single board computers. The current generation of embedded processors from companies like NXP, Qualcomm, MediaTek, and Intel offer integral AI accelerators and associated libraries. For example, the credit-card-size SOM-SMARC-Genio700 module uses MediaTek’s Genio 700 processor, which pairs eight Arm cores (2x Cortex-A78 + 6x Cortex-A55) with a Deep Learning Accelerator that delivers 4 trillion operations per second (TOPS) of AI performance. This lets complex AI run on small, fanless devices and even battery-powered systems.
For fog computing, higher end industrial computers offer greater processing power, connectivity, and dedicated AI coprocessing). For example, the Titan 300 TGL-UP3 AI fanless computer combines an Intel Tiger Lake UP3 processor with an Axelera AI Chip to deliver 120 TOPS through its Metis AIPU. This provides powerful AI processing at the local level.
On the software side, the Clea internet of things (IoT) software suite connects edge, fog, and cloud for advanced AI systems. It starts with Clea OS, a complete Yocto-based Linux platform that works across SECO’s edge and fog hardware, supporting both x86 and Arm processors. Clea OS makes it easier to deploy distributed AI by providing standardized development tools and back-end DevOps features for continuous integration and deployment.
The Clea ecosystem is cloud-agnostic and includes several key tools:
- For edge and fog AI nodes, Clea Edgehog delivers robust device monitoring and fleet management capabilities, making it easier to maintain and update devices at scale.
- At the cloud level, Clea Astarte provides sophisticated data orchestration, collecting, routing, and managing device data across a fleet of IoT devices. Astarte enables the collection, processing and analysis of large amounts of data, supported by Kubernetes for scalability and ScyllaDB for seamless business growth.
- Clea Portal provides a cloud-based front end for managing and monetizing the Edge AI fleet, with everything from data visualization to device control.
- Clea AI Studio is a visual programming environment that creates service flows based on processor vendor AI tools for deploying AI-powered applications on edge devices.
Developers can use the Clea software suite to build complete end-to-end IoT solutions and facilitate the deployment and operation of AI capabilities – whether the specific AI algorithm operates edge the edge, in the fog, or in the cloud. The suite handles everything from collecting data to creating user interfaces, streamlining the process of deploying edge AI.
Conclusion
While edge AI presents compelling advantages over traditional cloud or fog computing approaches, successful implementation requires careful consideration of hardware and software requirements.
SECO’s comprehensive solution stack enables organizations to optimize their Edge AI deployments according to their specific needs, providing the flexibility and scalability required in today’s rapidly evolving technological landscape.
Ready to start your edge AI design? Contact us today.