Jump-Start AI Designs with a Lattice Semiconductor and NVIDIA Collaboration

The ongoing arms race to develop the most powerful AI models can be mesmerizing, tempting observers to sit back and wait for the "right" model to win before deciding what to build with it. That would be a mistake for product designers who should take advantage of the opportunity to turn model predictions into something usable, trustworthy, and with practical value.

A collaboration between Lattice Semiconductor and NVIDIA signals a shift in product design for the AI era. By standardizing sensor-to-AI pipelines with a Sensor Bridge reference design, they’re lowering the barrier to building systems that perceive, interpret, and respond in near-real time. Applications can now be assembled more modularly, accelerating development and enabling more intelligent, responsive products.

As intelligence moves closer to where data is generated, the constraint shifts from building better models to designing how those models should behave once they’re embedded in real-world systems. What the system notices, prioritizes, or ignores are key design parameters that will determine whether an "AI product" is viewed as reliable, predictable, and useful.

Designers can implement the reference design utilizing Lattice Semiconductor’s LF-SNR-ETH-EVN (Figure 1), a CertusPro-NX Sensor-to-Ethernet bridge board which converts heterogeneous sensor signals into standardized, low-latency Ethernet streams that can be consumed by downstream edge AI systems.

Figure 1: Lattice Semiconductor's LF-SNSR-ETH-EVN takes input from heterogeneous sensors and transforms it into fast, structured data streams to be processed by NVIDIA edge modules. (Image source: Lattice Semiconductor)

The low-power FGPA-based hardware platform sits at the edge to transform diverse, low-level sensor signals into fast, structured data streams that can be processed over Ethernet in real time. It ingests raw sensor data, normalizes and packetizes that data, and streams it for low-latency, high-throughput processing with powerful graphics processing units (GPUs).

The Lattice board integrates with the NVIDIA Holoscan Sensor Bridge and edge computer modules to provide a full end-to-end stack for real-time sensor AI systems. This collaborative effort makes it easier to integrate new sensors and move data efficiently into AI inference pipelines on edge AI platforms like NVIDIA's IGX Orin and AGX Orin.

This approach transforms the cloud to a supporting role focused on training models, aggregating data across deployments, and managing updates and long-term system optimization, while edge modules can focus on real-time perception, interpretation, and response.

Creating sensor-to-AI designs

Designers can focus on rapidly building sensor-to-AI systems and streamlining how data flows from physical inputs into NVIDIA’s inference stack. Instead of relying on simulated inputs or abstract assumptions about how a system might behave, they can prototype experiences using live sensor data flowing through a working pipeline.

For example, a designer can begin with a real sensor setup, such as an industrial sensor or a camera, with data streamed through the CertusPro-NX bridge, for transmission over Ethernet, to a Holoscan-based application running on edge hardware for real-time AI inference processing. Traditionally, adding a new sensor would trigger a significant amount of engineering work, such as writing custom drivers, dealing with kernel-level integration, and building out bespoke data pipelines just to get the signal into a usable form. Holoscan reduces the burden with a standardized API and transport layer for continuously processing sensor data at the edge.

Sensor data is treated more uniformly as part of a real-time data stream, making it easier to integrate new sources into an existing AI pipeline without reworking the entire application architecture.

Translation layer

The Lattice FPGA-based board functions as a programmable translation layer between the physical world and the rest of the system, reducing the need to redesign hardware each time a new sensor is introduced. It includes pre-configured FPGA building blocks for handling and adapting sensor data, along with a complete software stack for collecting, moving, and processing that data on NVIDIA edge AI hardware.

This approach transforms sensor integration from a hardware constraint into a configurable design decision, significantly enhancing the system’s flexibility as product requirements evolve. Design teams can seamlessly integrate new sensors or add additional inputs during the development process without the need for extensive reworking of the entire architecture.

The ability to modify product behavior before full production systems are operational reduces custom integration work and allows for iterative improvements in system detection, action triggers, and uncertainty handling.

Conclusion

Product designers can’t afford to wait for the AI “model wars” to settle. The time is ripe to create AI-driven applications that can sense, decide, and act on real-time, real-world inputs. The collaboration between Lattice Semiconductor and NVIDIA provides designers with ultra-low latency pipelines that enable new categories of AI applications across more domains.