Singapore ranks top in the region and 9th globally in IMD’s Smart City Index 2025, particularly heralded for its well-connected and digitally advanced infrastructure – be it transport, or the essential digital applications that draw on a constantly updated repository of public information. Each of these is enabled by heavy integration of sensors playing a key role in real-time data collection and analysis that power the city-state’s everyday needs. Take, for instance, the 50-hectare Punggol Digital District. Powered by an Open Digital Platform, businesses there are given access to instant district data that enables innovation and optimises sustainable operations.
Keeping up with the pace, neighbouring cities, such as Hong Kong and Bangkok, are also embracing AI to improve the environment in which their urban community lives in. This has translated to heavy investments into capacity building for urban infrastructure, private-public partnerships to strengthen its innovation ecosystem, and bolstering education opportunities for advanced technologies to grow a local pipeline of talent that can tap into digital technologies solve long-standing challenges like sustainability and mobility.
Faced with steep competition and a narrowing divide in innovation leadership as AI access grows more ubiquitous, Singapore will need to reevaluate its Smart City strategy. This cannot be done without re-examining how the Republic is harnessing its data. After all, massive amounts of data that Singapore currently holds will allow it to tap AI, Machine Learning (ML), and data analytics to drive greater automation across devices, edge, and cloud. But, the key to this lies in eliminating impediments like computational bottlenecks and latency through designing and scaling industrial computers with greater levels of diversified compute.
Emerging computing trends
In Singapore’s highly digitised economy, the ongoing digitisation of edge applications is a strategic imperative for sectors ranging from precision engineering to biomedical sciences and logistics.
This process comprises sensorisation, the infusion of AI and ML across edge and cloud computing, human machine interfacing, multimedia experience, networking, and integration of Operational Technology (OT) and Information Technology (IT) domains, which often need different compute elements to perform optimally.
See also: Singapore leaps up the rankings in Bloomberg's innovation index
One area where Singapore has shown leadership is medical imaging, with the AI-enabled Medical Imaging Platform for Singapore public healthcare (AimSG) operated by Synapxe, Singapore’s national HealthTech Agency, in collaboration with National Health Group, National University Health System and SingHealth. Conceptualised to accelerate the adoption of AI in radiology Augmenting medical imaging workflows with AI can lead to improvement in clinical outcomes and productivity. For example, AI can help to quickly triage urgent cases for immediate attention by doctors, or it can serve as an additional layer to ensure accuracy while decreasing manpower needs.
Extensive sensorisation enhances efficiency and productivity for embedded applications. These wide-ranging sensors need to be interfaced and processed within milliseconds to achieve maximum responsiveness. Massive sensor deployments also feed Big Data algorithms that extract intelligence that drives improvement and next-generation product development.
Just like integrating driver-assistance technology into advanced cars, all components — camera, sensors, and display — must be meticulously engineered into one compact, highly optimised system. This ensures real-time hazard detection with the efficiency and low latency needed for functional safety.
See also: A Smart Nation ascending to the public cloud
However, achieving this seamless integration demands intensive, specialised engineering effort. Each new sensor or capability you wish to add, while enhancing performance and insight, necessitates significant re-engineering and integration work, representing a substantial, ongoing investment for superior, long-term operational advantages.
Flexible integration
A common approach is to leverage the rich ecosystem around processor architecture, alongside an adaptive compute platform that can execute real-time machine control, sensor interfacing and networking. This combination can be applied to use cases such as machine vision, industrial networking, robot controllers, medical imaging, smart city, security and retail analytics. Relying on conventional industrial PCs to arbitrate sensor data processing can introduce significant latency. For Singaporean businesses operating in mission-critical environments, such as precision manufacturing or critical infrastructure monitoring, these delays can be detrimental. The time required to ingest, process, and transfer sensor data to the accelerator introduces delays that can render real-time system response impossible, potentially impacting operational efficiency and safety.
Integrating sensor interfaces, AI processors, and network processing onto the FPGA-based adaptive compute platform consolidates these functionalities onto a single motherboard. This integrated approach eliminates the need for data to traverse disparate components, providing faster responses, greater accuracy, and lower power consumption.
Supportive ecosystem
Adaptive compute platforms that can handle real-time sensor processing, control, networking, and AI inference can help minimise latency, power consumption, and overall solution size. The result creates an efficient and powerful platform for embedded processing.
Platforms like adaptive heterogeneous processors are at the forefront of this evolution. Similar to building the central system that powers a super-robot, it’s a unified “brain” that simultaneously sees (through cameras and LiDAR), thinks (with AI), and acts (controls movement) – all in real-time. By integrating specialised computing power such as x86 with a network of direct sensor connections, a responsive, power-efficient, and compact platform is created, bringing unparalleled intelligence and speed directly to the edge, and enabling systems to master complex tasks and real-time information.
To stay ahead of the latest tech trends, click here for DigitalEdge Section
AMD and smart parking solutions provider Sun Singapore have partnered to build an AI-based solution that uses ultrasonic sensors and video for seamless traffic management. The solution provides edge devices with longer lifecycles, greater accuracy in vehicle license plate recognition, and low latency for real-time AI inference.
As Singapore doubles down on its Smart Nation authority, building robust core capabilities within its niche, high-growth sectors are non-negotiable. The synergy between its vibrant innovation landscape and the prowess of adaptive compute provides a strong foundation for developing cutting-edge applications that continue to lead in integrating advanced technology into daily life.
These advanced AI-driven analytics and next-generation smart city solutions will unlock new technological frontiers that drive Singapore's continued growth, global competitiveness, and resilience.
Steven Fong is the corporate vice president for APJ Embedded Business at AMD
