Smart infrastructure

Smart infrastructure is the integration of advanced technologies into cities, utilities, and transportation systems to optimize operations and management. It improves efficiency, reliability, and sustainability.

As urban populations grow, and demand for efficient, sustainable, and safe infrastructure systems intensifies, smart infrastructure becomes increasingly important. Traditional infrastructure systems face challenges in maintaining efficiency and meeting the demands of the growing population.

An important goal of smart infrastructure is to optimize performance and management using real-time data and automation, enhancing citizens' quality of life, and promoting sustainable development. Smart infrastructure provides a solution by leveraging technology to enhance functionality and resilience, enabling real-time monitoring, predictive maintenance, and automated management. It encompasses the use of sensors, data analytics, connectivity, and automation to optimize the operation and management of infrastructure assets.

The role of technology in building smart infrastructure and overcoming challenges

Smart infrastructure is revolutionizing urban environments, as it utilizes advanced technologies like the Internet of Things (IoT), Artificial Intelligence (AI), big data analytics, cloud computing, and machine learning, which are crucial for real-time data processing and decision-making. These technologies contribute to the development and management of advanced systems, addressing challenges, and enhancing overall efficiency and safety.

IoT in Smart Infrastructure

• IoT connects physical devices to the internet, enabling data collection and exchange.
• IoT sensors monitor parameters like traffic flow, energy consumption, and structural health.
• Real-time monitoring allows for immediate detection of issues like leaks or equipment failures.
• IoT-enabled systems enable predictive maintenance, reducing downtime and extending asset lifespan.
• IoT-enabled systems optimize resource usage based on real-time demand, improving efficiency and sustainability.

Artificial Intelligence (AI) in IoT

• AI enhances IoT systems by providing advanced data analysis and automated decision-making.
• AI algorithms analyze vast amounts of data to identify patterns, predict future trends, and generate actionable insights for infrastructure management.
• AI systems can autonomously manage and control infrastructure components, detecting anomalies and potential threats in real-time.

Big Data Analytics and Integration of IoT in Smart Infrastructure

• Big data platforms offer comprehensive data integration, enabling informed decision-making.
• Analyzing big data improves efficiency and planning, leading to cost savings and better resource allocation.
• AI and big data analytics facilitate the integration of IoT devices and big data analytics in infrastructure solutions.
• AI-driven security systems protect against cyber threats.
• IoT devices with built-in encryption and authentication mechanisms ensure data privacy.

Features of Smart Infrastructure

Smart infrastructure systems utilize sensors and IoT devices to collect real-time data, providing continuous information about the status and performance of various components. They can predict maintenance needs, optimize energy usage, and enhance connectivity through communication protocols like Zigbee, Z-Wave, and Wi-Fi. Automated systems manage infrastructure components with minimal human intervention, improving response times and operational efficiency. Advanced monitoring and analytics enhance public safety by detecting and responding to potential issues.

Smart infrastructure is a concept that combines advanced technologies and IoT solutions to create an interconnected, efficient, and intelligent urban environment. The block diagram below (figure 1) outlines the various components that contribute to the development of smart infrastructure. It comprises of various components, including smart utilities, smart buildings, smart homes, smart retails, smart appliances, smart streetlights, and smart streetlights. Each component represents a sector where advanced technologies and IoT solutions integrate to create an interconnected, efficient, and intelligent urban environment.

Figure 1: Elements of smart infrastructure (Source)

Smart infrastructure systems are designed to accommodate urban populations and demands, ensuring long-term viability. They work with various devices and platforms, facilitating easier integration and upgrades. Robust security measures protect data integrity and prevent unauthorized access, ensuring infrastructure reliability. Smart buildings use building management systems, HVAC controls, smart lighting, and security systems to enhance building efficiency. Smart homes use home automation devices, smart appliances, security systems, and energy management systems to improve energy efficiency. Smart appliances use connected devices, remote monitoring systems, and energy-efficient appliances. Lighting systems adjust lighting based on real-time data, reducing energy consumption and enhancing public safety.

The Adaptive System-On-Module (SOM)

AI-enabled applications are revolutionizing cities by managing safety and alerting emergency services. These applications can be developed using chip-down development, which involves selecting specific silicon devices and creating a customized circuit board. However, this method can be time-consuming and costly. To save time and costs, design teams can opt for integrated solutions like Multi-Chip Module (MCM), System-in-Package (SIP), Single-Board Computer (SBC), or System-on-Module (SOM).

An SBC is a standalone computing system with a microprocessor, memory, and I/O, typically not designed for production deployment, while a System-on-Memory (SOM) is a production-ready platform that connects to a larger solution. They are designed to fit into larger edge applications, offering flexibility, ease-of-use, and reduced time-to-market compared to off-the-shelf solutions. When combined with adaptive SoCs, adaptive SOMs provide a comprehensive, production-ready platform for AI-enabled edge applications.

AMD Kria™ SOMs offer significantly more system-level flexibility than microprocessors or GPU-based SOMs available in the market. They provide numerous additional features to accelerate development. There is support for a pre-built and scalable Linux infrastructure, whether users prefer the Ubuntu and Ubuntu Core distributions by Canonical or the Linux kernel managed by AMD with AMD PetaLinux Tools (as shown in figure 2). Additionally, custom builds using Yocto are supported. These options come with practical system utilities such as temperature and performance monitors, quality-of-service configuration for DDR memory and other peripherals, over-the-air firmware update capabilities, and built-in self-test functions. The most significant attribute of Kria SOMs is the growing library of accelerated applications found on the AMD App Store, both for free and for a fee, which can be easily loaded into a Kria SOM.

Figure 2: Pre-Built Linux Infrastructure for AMD Kria™ SOM

Abstraction Layers of Adaptive SOMs

Adaptive SoCs offer three degrees of freedom: software programmability, hardware programmability, and scalability for embedded platforms. As shown in figure 3, they include a comprehensive set of design and runtime software, enabling the creation of flexible, efficient systems. APIs and pre-built platforms offer access to AI models for common inference functions.

Figure 3: Adaptive SOMs can be programmed from several abstraction levels (Source)

Software developers can accelerate their design cycles by using pre-built configurations for adaptive SoCs, and advances in software tools, libraries, and frameworks allow design teams to use adaptive computing without burdening hardware engineers. An adaptive SoC provides a simple, out-of-the-box experience for developers in familiar environments like Python, C++, TensorFlow, and PyTorch.

AMD Kria™ K26 SOM: A Powerful Solution for Smart Infrastructure

AMD Kria™ adaptive SOMs are built around the AMD Zynq™ UltraScale+™ MPSoC architecture and give developers access to a turnkey adaptive computing platform. By standardizing the core parts of the system, developers have more time to focus on building features that differentiate their technology from the competition. Kria SOMs are an integrated embedded, pluggable, nearly-credit-card-sized, Arm^® SoC-based solution. They provide a secure and production-ready multicore Arm processing and FPGA platform, including memory, power management, and your choice of a Yocto or Ubuntu Linux infrastructure to build accelerated AI-enabled applications at the edge.

Figure 4: AMD Kria™ SOM product family (Source)

The AMD Kria™ K26 System-on-Module (SOM) excels in supporting artificial intelligence (AI) and machine learning (ML) algorithms, which are critical for predictive maintenance and optimization in smart infrastructure. By leveraging the Kria K26 SOM's powerful processing capabilities, these algorithms can analyze real-time data to predict equipment failures, optimize resource usage, and improve overall system efficiency. The Kria K26 SOM is a high-performance AI system designed for Vision AI applications. It combines AI performance with adaptability to keep pace with evolving algorithms and sensor requirements. The Kria K26 SOM is available in commercial and industrial grades and is designed for high-volume edge deployments.

It delivers up to 1.4 TOPS for AI tasks and integrates a dedicated H.264/265 video codec for efficient video processing. With 245 I/Os, it can connect up to 15 cameras, integrate with networks at 40 Gb/s, and leverage USB peripherals. Developed for software developers, the K26 SOM features pre-built accelerated applications for common vision functions, streamlining development processes and help to improve time-to-market.

Figure 5: Picture of the AMD Kria™ K26 SOM (Source)

AMD Kria™ KV260 Vision AI Starter Kit

AMD offers a starter kit that consists of a non-production AMD Kria™ SOM mated to a predefined carrier card with various interfacing options to connect sensors or other peripherals. The starter kit is the easiest way to get started with Kria SOMs, especially with Vision AI applications, and provides a path to move to a custom user-defined production carrier card that is developed for the target application.

Figure 6: AMD Kria™ KV260 Vision AI Starter Kit (Source)

The development platform featuring the AMD Kria™ K26 SOM, the AMD Kria™ KV260 Vision Starter Kit, is designed for advanced vision application development without requiring complex hardware design knowledge. This kit focuses on applications such as security, smart cities, traffic management, and retail analytics. These applications have driven design decisions on the Kria KV260 Vision AI Starter Kit, including elements like the chosen connectors for I/O interfacing.

Figure 7: Block Diagram of AMD Kria™ KV260 Vision AI Starter Kit

Figure 9 shows a block diagram of AMD Kria™ KV260 Vision AI Starter Kit. It is powered by an AMD Zynq™ UltraScale+™ MPSoC hardware root of trust (RoT) and Infineon TPM2.0 for secure boot ,with 4 GB DDR memory, and 512 Mb QSPI primary boot memory. It features a Vison Ready design, multi-camera support, 3 MIPI sensor interfaces, USB cameras, built-in ISP component (OnSemi), Raspberry Pi camera interface, HDMI and DisplayPort outputs, flexible connectivity via 1 Gb Ethernet and USB 3.0/2.0, and an accessible Pmod ecosystem.

Conclusion

Explore how AMD is revolutionising smart infrastructure with the AMD Kria K26 SOMs, delivering unparalleled performance and flexibility for your applications. Click here