Expanding AI's Capabilities Beyond the Cloud with Edge AI

What does Edge AI mean in the context of Industrial IoT Ecosystems?

Within the evolving technological landscape, a notable transformation is underway, reshaping the convergence of Artificial Intelligence (AI) and IoT. This shift is centred around the concept of Edge AI, which broadens the horizons of AI beyond the limitations of cloud computing. Edge AI involves the deployment of AI applications on tangible, real-world devices, where computations and AI inferences take place at the outer edges of the network, near the data sources. Essential to this transformation are edge devices, representing a new generation of intelligent tools tailored for edge network operations. These devices are equipped with robust processing capabilities and incorporate AI functionalities for localized data analysis, minimizing latency, facilitating instantaneous decision-making, and optimizing network resource usage. In contrast to traditional IoT devices and the transmission of data to remote cloud servers, edge computing empowers AI models to function directly on these devices, thereby enhancing operational efficiency and the ability to process data in real-time. This positions Edge AI as a catalyst for growth across various industries, offering businesses newfound capabilities in an increasingly data-driven environment. 

Key benefits of Edge AI 

In the industrial context, the Internet of Things (IoT) paradigm has been widely adopted, leading to the emergence of an ecosystem referred to as Industrial IoT (IIoT), harnessing data sourced from machines and systems to elevate manufacturing and industrial processes. This ongoing transformation entails the fusion of high-performance machinery with advanced sensors and control electronics, thereby bolstering business intelligence. Nevertheless, as IIoT networks become increasingly sophisticated, they introduce a set of challenges encompassing concerns such as latency, network availability, and data security. 

Through the deployment of AI algorithms on edge devices, Edge AI serves as an effective response to these challenges associated with cloud-based AI. Tailored for real-time, low-latency AI tasks, the convergence of AI and edge computing offers an array of advantages, including: 

  • Reducing Data Transmission Expenses: Edge AI effectively lessens the requirement for extensive data transfers to remote data centres, resulting in a significant reduction in network costs. 
  • Ensuring Connectivity Resilience: In conditions marked by intermittent network availability, devices integrated with Edge AI sustain their operational capabilities, guaranteeing uninterrupted functionality even amidst varying network conditions. 
  • Elevating Data Privacy and Security: Through localized data processing, sensitive information remains confined within the boundaries of edge devices, successfully mitigating potential risks associated with data interception during transmission. 
  • Granting Device Autonomy: Edge AI bestows devices with the capability to make intelligent decisions at a local level, ultimately diminishing reliance on centralized cloud resources and enhancing device reliability. 
  • Minimizing Latency: An ideal choice for applications requiring rapid decision-making, processing data at the edge delivers swift insights from incoming data streams.

Edge AI applications Transforming industrial efficiency 

With these core benefits harnessed, Edge AI presents transformative potential across a range of industrial scenarios, facilitating efficient data processing, real-time decision-making, cost-efficiency, and adaptability. 

For example, consider industrial settings where the financial implications of machinery downtime are substantial. In such contexts, Edge AI emerges as a highly viable solution. By directly implementing AI algorithms on edge devices like sensors and controllers, continuous monitoring of equipment health becomes a reality. Real-time detection of anomalies and potential malfunctions allows for the implementation of predictive maintenance strategies. This proactive approach serves to minimize operational disruptions, trim maintenance costs, and prolong the lifespan of critical assets. 

In the context of modern manufacturing, where stringent quality control standards are imperative, we can find another example. Edge AI empowers manufacturers by enabling the real-time monitoring and analysis of production processes. Cameras and sensors integrated into the manufacturing line capture data that undergoes instant on-site processing via AI algorithms. Swift identification of any variations or defects empowers manufacturers to promptly take corrective actions. This, in turn, leads to a reduction in waste, an enhancement of production efficiency, and increased customer satisfaction. 

In addition, the fusion of Edge AI and autonomous robotics heralds a new era of automation across various sectors. By enabling robots to locally process sensory data and make decisions at the edge, these machines become highly adept at navigating complex environments. This capability holds particular significance in sectors like logistics and warehousing, where efficient and real-time decision-making is paramount. Reduced dependence on cloud connectivity empowers robots to independently perform tasks, ensuring heightened productivity and operational flexibility. 

Future challenges and trends of Edge AI 

Nevertheless, integrating AI models into edge devices brings about challenges when it comes to unlocking the full potential of Edge AI. On edge devices, where computational resources are limited, and power constraints come into play, efficiently executing AI poses complications. However, there is no shortage of potential effective solutions: 

  • The method of Federated Learning enables AI models to undergo collaborative training across edge devices without the transmission of raw data to a central server. This approach not only safeguards data privacy but also harnesses the collective knowledge of distributed devices to enhance the model's performance.  
  • The Edge Caching technique notably mitigates concerns about latency, reduces response times, and minimizes network congestion: by storing frequently accessed data and models directly on edge devices, it becomes possible to fulfil repeated requests locally, eliminating the need to access remote servers.  
  • The promising approach of Model Quantization optimizes efficiency by converting high-precision floating-point model parameters into lower-precision fixed-point formats. Quantized AI models demonstrate efficient operation across various hardware architectures, ensuring faster, power-effective inferences with a high degree of accuracy.

Moreover, as we look ahead, it's evident that innovation in edge AI chips is an ongoing process, driven by the seamless integration of Edge AI capabilities into diverse industries' operations. This trajectory signifies an impending era marked by dynamic transformation, where advancements in hardware, software, and AI algorithms converge harmoniously, driving innovation across a wide spectrum of applications. 

Traditional industrial CPUs and GPUs, ill-equipped to meet the escalating data analysis requirements of evolving networks, are being overshadowed by the next generation of AI chips meticulously crafted for edge computing. These chips stand poised to tackle these challenges by processing data locally. 

Advancements in hardware design, underscored by augmented computational capabilities, simplify operations like Edge Machine Learning Operations (MLOps) techniques. MLOps, which combines DevOps principles with ML tools, streamlines the process of constructing, testing, deploying, and monitoring ML models. 

Addressing the challenge of limited resources on edge devices, the latest generation of devices exhibits the capability to accommodate substantial models and are endowed with specialized processors optimized for inference tasks. These processors are complemented by SDKs and toolsets tailored for artificial intelligence, further streamlining the integration of MLOps pipelines for efficient model deployment. 

SECO’s offering in advancing Edge AI capabilities 

AI chipsets unlock remarkable processing capabilities, yet they require carrier boards and related hardware for seamless integration into real-world applications. 

SECO's extensive array of edge computing products, ranging from modules to complete solutions, eliminates the need for system designers to develop this foundational interface hardware. Instead, they can focus on tailoring AI software and hardware to the specific requirements of their application, sparing themselves from reinventing the computing wheel. 

SECO's offerings also encompass Edge AI-ready solutions like the Titan 240 APL, a Fanless Embedded Computer featuring Intel® Atom™ X Series, Intel® Celeron® J / N Series, and Intel® Pentium® N Series processors. Additionally, SECO's off-the-shelf catalogue includes a new line of purpose-built AI products, such as the Titan 300 TGL-UP3 AI: a Fanless embedded computer equipped with 11th Gen Intel® Core™ and Intel® Celeron® SoCs, complemented by the Axelera AI Chip. Powered by a single Metis AIPU capable of delivering up to 120 TOPS, this product is the result of SECO's collaboration with Axelera AI and features a potent dedicated AIPU (AI Processing Unit). 

Leading the way in Edge AI development

Edge data processing delivers energy, bandwidth, and storage savings, optimizing physical infrastructure. SECO assists its customers in seamlessly integrating AI into their products, unlocking fresh technological possibilities, and enabling innovative business models. 

SECO also provides expertise in AI software implementations. SECO's Clea comprehensive package serves as a robust IoT platform, facilitating edge MLOps, edge and cloud orchestration, AI model deployment, and the monitoring and validation of updates. Clea presents a robust solution for device management, from edge to cloud, with a strong emphasis on security.  

With over a decade of experience in mission-critical infrastructures, Clea offers a remarkable degree of flexibility that is agnostic to both cloud and device considerations, making it suitable for a diverse array of use cases. It effectively reduces the reliance on cloud infrastructure by enabling localized data processing, thereby delivering cost savings, energy conservation, and a heightened level of security. Clea takes on the responsibility of orchestrating data science workloads, optimizing the physical infrastructure, and facilitating data-driven decision-making through advanced analytics. 

Where could Edge AI take your IoT project? 

In summary, Edge AI is revolutionizing the way businesses utilize artificial intelligence, playing a crucial role in nurturing intelligent and responsive IoT ecosystems. The significance of Edge AI resides in its capacity to deliver real-time data processing, cost-effectiveness, heightened privacy and security, all with reduced latency, while also empowering device autonomy. Looking ahead, Edge AI presents promising prospects as hardware, software, and AI algorithms converge to accelerate advancements across various domains. 

Contact our team of experts today to embrace Edge AI and unlock the potential of data-driven innovation for your IoT projects.