Scale your NVIDIA platform for multi-sensor AI
NVIDIA Lattice30 Jun 2026
Compute power from NVIDIA isn’t always enough for multi-sensor systems. Here’s how to combine it with FPGA-based sensor aggregation and Holoscan to unlock real-time, high-input applications.
Modern AI applications rely on multiple high-speed data sources – cameras, LiDAR, radar and more.
While NVIDIA platforms provide powerful compute, they often require additional support for:
- Aggregating multiple sensor inputs
- Normalizing data streams
- Reducing system bottlenecks
The solution
Holoscan + FPGA = Scalable sensor integration
NVIDIA Holoscan enables real-time data processing pipelines but relies on efficient input orchestration. FPGAs act as the bridge by:
- Consolidating and preprocessing sensor data
- Standardizing communication in NVIDIA systems
- Enabling deterministic, low-latency performance
Start with Lattice FPGAs for efficient edge designs
Lattice FPGAs provide a lightweight, power-efficient way to enable multi-sensor systems.
Key features
FPGA-based interconnect
Enables adaptable interfaces for diverse sensors and performs data packetization with minimal delay
Optimized GPU access
Moves data efficiently from DRAM to compute engines for rapid AI inferencing
End-to-end efficiency
Reduces bottlenecks between sensor ingress and decision-making output
Scalable and modular design
Allows developers to integrate multiple sensors without redesigning the entire system, reducing time to market
Best for:
- Edge AI and embedded vision
- Low-power environments
- Rapid prototyping and deployment
Additional FPGA partners
Altera
- High-performance, scalable architectures
- Ideal for complex, high-bandwidth systems
Microchip
- Strong in industrial and safety-critical environments
- Deterministic performance and reliability
Build your multi-sensor system faster
Article tags
Global
Article
Field Programmable Gate Arrays - FPGAs
NVIDIA
Lattice Semiconductor
