Deploying Edge AI: An Overview of Industry Trends and Hardware Selection
September 05, 2024
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Edge AI is being increasingly deployed across various sectors, including healthcare, manufacturing, retail, and smart cities. This trend is driven by the need for real-time data processing and decision-making at the source of data generation, facilitated by deep learning at the Edge.
Solutions that offer scalability and interoperability are gaining the most traction in this space. Businesses are seeking Edge computing frameworks that can seamlessly integrate with existing infrastructure and scale as demand grows, ensuring long-term viability and cost-effectiveness.
To make this scenario viable, system manufacturers are integrating 5G networks. This rollout is a significant catalyst for Edge computing. With its high-speed connectivity and low latency, 5G enhances the capabilities of Edge devices, enabling faster data processing and more robust applications, particularly in smart factories and, on a larger scale, smart cities.
To realize Edge AI, there’s now a growing emphasis on developing AI models that are optimized for Edge devices. This includes creating lightweight algorithms that can run efficiently on devices with limited computational power and energy resources, ensuring effective performance without relying on Cloud computing resources.
Edge computing addresses a key priority for businesses and cities: data privacy and security. These concerns are reduced by processing data locally, minimizing, but not eliminating, the need to transmit sensitive information to centralized servers. This trend is particularly relevant in sectors handling sensitive data, such as healthcare and finance.
How to Choose the Right Edge Hardware
As you might expect, there’s been a recent surge in the development of specialized hardware designed for Edge computing, such as Edge servers and AI accelerators. These devices are tailored to meet the demands of edge environments, offering enhanced processing power and energy efficiency. However, platforms of this type are quite complex and not for the faint of heart.
Edge AI faces several challenges, particularly in achieving real-time results, operating in harsh environments, and maintaining low power consumption. Delivering real-time results requires extremely rapid processing capabilities, which can be constrained by the sometimes-limited computational power of current Edge devices. Make no misstate: ensuring low latency while processing complex AI algorithms locally is a significant hurdle. Optimizing models to run efficiently on these devices without sacrificing accuracy or speed is critical, yet challenging.
Edge devices are often deployed in environments that are not conducive to standard computing operations, such as extreme temperatures, humidity, dust, and vibration. Ensuring that these devices remain operational and reliable under such conditions requires robust, ruggedized hardware. This adds to the complexity and cost of deployment and maintenance.
In addition, it’s imperative to keep the power consumption to a minimum, as such computations can quickly result in higher-than-expected power, and with systems that are difficult to cool. Platforms running AI computations pose a challenge to balance performance with power consumption. Developing low-power AI processors, such as those from Intel, and optimizing algorithms for energy efficiency are essential, yet difficult, tasks.
Edge AI in the Factory
Edge AI offers significant benefits to factories and smart cities by enhancing efficiency, safety, and decision-making capabilities. For example, Edge AI enables real-time monitoring of machinery and production lines, identifying potential issues before they lead to breakdowns. Predictive maintenance reduces downtime and maintenance costs, ensuring smooth operations.
By analyzing data from sensors and cameras on-site, Edge AI can detect defects and anomalies in products instantly, ensuring higher quality standards without the delays of sending data to a central server. It also facilitates the automation of complex tasks by providing quick decision-making capabilities directly on the factory floor, leading to increased productivity and operational efficiency. And finally, workers can be monitored in real-time, identifying hazards and preventing accidents through immediate alerts and interventions.
Edge AI in Smart Cities
When it comes to smart cities, Edge AI can process data from traffic cameras and sensors to optimize traffic flow, reduce congestion, and improve public transportation efficiency. It can also provide real-time updates to commuters. By analyzing those video feeds and as well as other local sensor data, Edge AI can detect and respond to incidents such as crimes or emergencies faster, improving response times and overall safety.
Edge AI can optimize energy use in smart buildings and street lighting by adjusting settings based on real-time data, leading to significant energy savings and reduced carbon footprint. It can process data from environmental sensors to monitor air quality, noise levels, and other factors, enabling timely interventions to maintain healthy living conditions.
Options Abound But Choose Wisely
An engineer has many different areas to consider and many decisions to make when choosing an Edge AI platform, especially when it comes to the CPU/GPU, ruggedness, security, integration, and a stable supply chain. Ensuring that your CPU can handle the load is job one. Compatibility with leading CPUs and GPUs like those from Intel can provide the needed performance. It’s important to verify that the platform is compatible with the necessary AI frameworks and libraries to facilitate the seamless development and deployment of AI models.
Choose a platform that is designed to withstand harsh conditions, including extreme temperatures, dust, moisture, and vibrations, ensuring reliability in industrial or outdoor environments. To achieve this, look for certifications such as IP (ingress protection) ratings, that indicate robustness.
When it comes to data protection, ensure that the platform offers robust security features, such as encryption, secure boot, and trusted platform modules (TPM), to protect sensitive data and prevent unauthorized access. And, of course, check for support of secure software development practices and regular updates to patch vulnerabilities.
The platform should seamlessly integrate with existing sensors, cameras, and other hardware components used in the system. Verify compatibility with the current software stack and ease of integration with Cloud computing services, IoT platforms, and other enterprise systems. It also ensures that the platform can communicate and operate with other systems and protocols in place.
A stable supply chain is often overlooked and taken for granted. However, it is crucial to partner with reputable suppliers known for their consistent delivery and support. Evaluating their track record and reviews from other customers is essential. It's also important to ensure that components and the platform will remain available throughout the projected life of the product, thereby avoiding obsolescence and supply disruptions. Additionally, the level of technical support, warranty, and after-sales services provided by the supplier should be carefully considered to maintain ongoing reliability and performance.
ASUS IoT is one of the platform providers whose edge AI systems combine the latest in Intel CPU/GPU computing with AIoT. The company’s platforms maximize the scalable performance offered by the Intel CPUs, even for diverse applications, including real-time AI inferencing at the Edge.
Many of the ASUS IoT systems are designed with a rugged, fanless, anti-vibration build, wide temperature support, and low power consumption, all characteristics needed for factory automation, machine vision, video analytics, and smart cities. ASUS IoT ensures robustness and reliability for the most challenging scenarios, driving innovation and efficiency in this new era of Edge AI and Edge computing. Contact ASUS IoT to learn more.
