[Blog] Building Smarter Robots: The FPGA Advantage in Real-Time Response

Robots have rapidly evolved from science fiction into a cornerstone of modern industry. Today’s autonomous systems can execute complex tasks with minimal human oversight – transforming how we work, live, and move. But achieving this level of intelligence and reliability in real-world environments requires more than just advanced software. It demands robust hardware, deterministic processing, and scalable system architectures that can support safe, real-time decision-making under demanding conditions.

Whether it’s an autonomous mobile robot (AMR) navigating a warehouse, a robotic vacuum cleaning a home, or a self-driving car transporting passengers, these machines are entrusted with increasingly critical responsibilities – often without direct human control. Their success hinges on an essential capability: real-time responsiveness.

Without the ability to react instantly to dynamic inputs, robotic systems risk failure in time-sensitive scenarios. That’s why engineers must design solutions that prioritize low latency, edge intelligence, and reliable motor control – starting with the right hardware foundation.

Enabling Real-Time Response in Robotics
Engineers must understand the capabilities required to support real-time robotic response and the hardware solutions – like Field Programmable Gate Arrays (FPGAs) – that are capable of delivering them. These include:

• Sustainable and efficient power. While a robotic vacuum cleaner’s battery dying mid-clean isn’t ideal, it’s more frustrating than life-threatening. However, a power failure in an autonomous car’s navigation capacity or industrial machinery’s ability to sense a nearby human and quickly cease operations could have serious ramifications. Without a reliable and continuous supply of power, there’s no guarantee that robots will be able to react to time-sensitive stimuli.
• Support for AI at the edge. As autonomy becomes more critical to operations, developers are beginning to enable robotic self-sufficiency with AI and machine learning models. These solutions allow for more refined and dynamic autonomy, where robots can sense and react to real-time stimuli. Since robotic machinery operates at the edge of connected infrastructure, however, it must have adequate computing capacity to support AI and ML integration.
• Low latency connectivity. Any connected system can experience latency issues during data transfers, and these micro-delays can interrupt autonomous activities. High-speed capabilities are mission-critical to safe operation within industrial facilities, for example, as any latency in the system could be the difference between business-as-usual and a catastrophic accident. Avoiding these delays is paramount to leveraging distributed robotics solutions entrusted with high-risk tasks.

While each of these conditions is important in its own right, meeting all three is essential for enabling effective and reliable real-time robotic response.

The FPGA Advantage
Engineers tasked with building real-time responsiveness into robotic applications must create builds that support low latency, edge operability, and high-throughput data processing. While traditional general-purpose processors can help enable these capabilities, they do not always come with the power efficiency and deterministic processing capacity required of edge applications.

FPGAs present a powerful, dynamic solution for today’s robotic applications, with devices like Lattice Certus™-NX, Lattice Avant™-E, and Lattice CrossLink™-NX being purpose-built to support the needs of real-time robotic motor, motion, and sensor control at the edge. Lattice’s robotics-ready FPGAs offer:

• High-performance parallel processing, or the capacity to execute multiple deterministic processing functions in at the same time. This allows for a single chip to simultaneously execute multiple critical processing duties, reducing bottlenecks associated with sequential processing, overall power demands, and the space required to accommodate complex computing functions.
• Customization and reconfigurability, enabling developers to tailor specific FPGA chips to defined robotic workloads – all while maintaining the flexibility to adjust or upgrade capabilities without needing to change hardware. This supports iterative robotic development and faster time-to-market while keeping robotic devices adaptable to new tasks and/or protocols as needed, throughout their life cycle.
• High-speed I/O support, enabling seamless integration with edge sensors, actuators, and other components through protocols like PCIe®, Ethernet®, LVDS, MIPI®, and more. This helps streamline component connectivity while simultaneously reducing latency by removing any intermediate chips.
• Edge AI processing, supported through customizable FPGA fabric, sensor fusion enablement, and dedicated internal RAM. These features help developers offload processing requirements from centralized servers, instead enabling the on-device decision-making that’s crucial for real-time autonomy.
• Dedicated security features like encryption, secure boot, and Hardware Root-of-Trust (HRoT) capacity, which are critical to reliable and sustainable robotic solutions. Integrating these capabilities at the chip level helps prevent tampering and supports real-time protection, detection, and recovery across robotic devices at the edge.

Ultimately, FPGAs can provide the efficiency, flexibility, and processing capacity required for real-time robotic solutions in a compact, sustainable, and secure package.

FPGAs In Action: Exploring Real-World Robotics
From Industrial automation to medical diagnostics and autonomous vehicles, FPGAs are powering the next generation of intelligent, responsive robotic systems. Their unique combination of low latency, power efficiency, edge AI support, and secure processing makes them ideal for high-stakes environments where real-time decision-making is non-negotiable.

FPGAs are advancing Industrial autonomy and real-time responsiveness, proving critical to efforts to bring digital-first Industry 4.0 into reality. FPGAs enable key features for AMRs and delivery robots, including real-time motor control and path planning, sensor fusion between lidar, radar, and camera input, and edge AI integration for object recognition and obstacle avoidance. FPGAs are also used to support deterministic controls for robotic arms and conveyor systems, and real-time robotic communication through industrial protocols like TSN-Ethernet and EtherCAT®.

These flexible chips are also used in the medical field, enabling real-time control for surgical robots, precision instrumentation, and diagnostic machinery. They can be programmed to support embedded AI models for medical imaging, gesture recognition, or even autonomous monitoring, all while ensuring machinery can react to patient needs in a timely manner.

In the Automotive industry, FPGAs are used to reduce the load on the central CPU and allow vehicles to respond more quickly to real-time stimuli. They can also be programmed to help support autonomous driving and driver assistance capabilities like lane detection and pedestrian/obstacle tracking, in addition to smart security functions like external monitoring systems.

As engineers and developers continue to push the boundaries of autonomy, FPGAs provide the flexible, scalable foundation needed to bring science-fiction-level robotics into everyday reality.

To learn more about how Lattice FPGAs can accelerate your robotic innovations, contact our team today.