Thanks to the continued expansion of artificial intelligence (AI), today’s devices are only getting smarter. Physical AI is expanding automated capabilities beyond digital applications, operating in robots, drones, industrial automation platforms, autonomous vehicles, and other devices. It’s even reached the dental hygiene space in the form of a $500 AI-powered toothbrush.

Whether a toothbrush, drone, or otherwise, these devices rely heavily on sensors to perceive the world around them. And while AI can help enable more autonomous and responsive devices, synchronizing, transporting, and processing sensor data at the edge is no simple task. System designers must create efficient pathways between sensors and compute if they want physical AI applications to help rather than hinder their devices.

In our latest LinkedIn Live panel, experts from Lattice, Arrow Electronics, Citrobits, Tecphos, and tinyVision.ai discussed why the future of physical AI depends on rethinking the architecture that connects perception to processing power.

The Complexity of Sensor-Rich Systems
As smart robotics, autonomous systems, and other AI-enabled devices continue to proliferate our lives, they drive increased demand for sensors. These sensors – cameras, lidar, radar, and others – serve as critical perception inputs for physical AI systems, providing the data required to understand and respond to the device’s surroundings in real time.

The richer the perception data required for these smart devices, the more sensors are needed throughout the system design. While additional sensors help improve coverage and situational awareness, they also increase demands. Each source adds to the bandwidth demands, data streams, and synchronization requirements needed within limited device infrastructure.

At the same time, processor vendors are expanding AI compute capabilities in increasingly compact devices, leaving system designers with the need to support more input with limited connectivity options. This has created a growing mismatch between the amount of sensor data being generated and the I/O resources available to ingest it. Designers must manage bandwidth and latency requirements, limited power budgets, thermal constraints, wiring complexity, and overall system costs, all while trying to incorporate more distributed sensors.

With only so many available I/O interfaces and potentially significant wiring costs, it is incredibly difficult to create edge systems that can transport data efficiently from sensors to processors. Doing so in a manner that maintains performance and efficiency without exacerbating bandwidth and power constraints requires a more intentional design.

Building Sensor-to-Compute Pipelines
Traditional centralized edge architectures are increasingly strained by high-volume, multi-sensor data movement. Edge vision systems, for instance, cannot simply route raw sensor data streams directly into a CPU, GPU, or NPU without creating bandwidth, latency, and power challenges. As sensor counts rise, centralized architecture is becoming increasingly difficult to scale.

Designers hoping to address this challenge are reconsidering how vision pipelines ingest and transport data from the sensor to the primary compute platform. Rather than relying on a single processor to accommodate every sensor connection, the architecture can use flexible interface devices to aggregate inputs, synchronize data streams, and adapt multiple sensor interfaces to the connectivity available on the host processor. This creates a more efficient path for data to move through the system while allowing designers to support different sensor types and interface requirements.

This is particularly important in multi-sensor systems, where accurate timestamping and synchronization help perception algorithms correlate data collected from different sensors at different times. Cameras, lidar, radar, and other sensors may use different connections and generate data with different bandwidth and timing requirements. A flexible ingestion layer can bring these inputs together and deliver them to the host through the interface best suited to the application, creating a more scalable foundation for additional sensors.

Different connectivity options, including MIPI, PCIe, Ethernet, and USB, each bring trade-offs around bandwidth, latency, reach, synchronization, driver complexity, and power. The right architecture depends on the application, but the broader design challenge remains the same: moving high-volume sensor data into compute efficiently while preserving performance, scalability, and system responsiveness.

However, the solution brings with it another challenge: finding hardware that’s flexible and capable enough to handle the job.

How FPGAs Address Sensor and Interface Challenges
This is where field programmable gate arrays (FPGAs) play a critical role. As vision systems become more distributed, designers need a flexible way to connect different sensor types and deliver their data through the interfaces available on the host processor. FPGAs provide that adaptability, serving as an intermediary connectivity layer that can:

• Aggregate inputs from cameras, lidar, radar, and other sensors.
• Bridge sensor and host connectivity standards.
• Synchronize data streams.
• Route data to CPUs, GPUs, NPUs, and other host processors.

Just as importantly, FPGAs can provide a flexible ingestion layer between sensors and host processors. They can accept inputs from multiple sensor types, align and synchronize data streams, and bridge those inputs to host interfaces such as MIPI, PCIe, Ethernet, or USB. This allows designers to connect the right sensors to the right compute platform without requiring the host processor to natively support every sensor interface.

FPGAs can also help overcome I/O limitations at the edge by bridging and expanding connectivity between sensors and compute, enabling support for a wider range of sensor and host interfaces. Their reconfigurable nature allows systems to adapt as sensor requirements, interface standards, and host platforms continue to evolve without requiring designers to replace the broader hardware architecture.

Ultimately, FPGA-based architectures can simplify sensor integration and data movement, creating a scalable connectivity foundation for physical AI systems with diverse sensors and host processors.

The Path to More Effective Physical AI
As physical AI systems continue to evolve, success will be rooted in the efficient ingestion, organization, and movement of sensor data at the edge. By enabling sensor aggregation, synchronization, and connectivity across diverse sensor and host interfaces, FPGAs can help provide the infrastructure required to support the next generation of robotics, industrial automation, machine vision, and beyond.

To learn more about the sensor-rich evolution of physical AI deployments, watch our full LinkedIn Live panel discussion. To explore how FPGAs can better enable smart, multi-sensor devices, contact our team today.