- What AI technology should I choose for my robot?

Choose NPUs for efficient neural inference, GPUs for complex vision, and FPGAs for deterministic pipelines. Use MPUs/CPUs as the orchestration layer for system logic and middleware.

- How can I improve performance without increasing power consumption?

Use quantisation and model pruning to reduce compute load. Offload inference to NPUs and validate real-duty-cycle power behaviour instead of relying on peak specifications.

- Which industrial AI compute platforms[KP1] are available?

Industrial AI compute platforms range from SOM/COM modules (SMARC, OSM, COM-HPC) to ready-to-use motherboards and certified box PCs. While modules offer maximum design flexibility, motherboards and box PCs provide faster integration, pre-validated interfaces and reduced certification effort for plug-and-play deployments.

- How easy is it to switch between AI suppliers[KP2] or silicon vendors?

With SOM/COM-based designs (SMARC, OSM, COM-HPC), standardised pinouts enable vendor flexibility with minimal carrier redesign, and abstracted SDKs further reduce lock-in. For system solutions such as motherboards or box PCs, no cross-vendor standards exist – flexibility therefore depends on choosing a partner with a broad, closely aligned platform portfolio.

- How do I deploy a digital or virtual twin?

Use compute platforms that support GPU acceleration and synchronised sensor input. Stable latency and predictable timing are essential for accurate twin behaviour.

- Do I need an NPU, or is an MPU enough?

If your robot performs real-time inference, an NPU delivers far higher efficiency and lower latency. For basic logic and low-duty AI tasks, an MPU may be sufficient.

- What is the difference between TOPS and cores?

TOPS measure neural inference throughput; cores measure general CPU/MPU compute capacity. They cannot be compared directly – use the metric that matches your workload.

- How do I prevent latency spikes in my compute pipeline?

Choose compute architectures optimised for sustained performance, not peak numbers. FPGAs or real-time MPUs help remove jitter in control loops.

- Is a modular compute platform faster to integrate than a custom board?

Yes – SOM/COM modules remove most high-speed and power-integrity risks. Integration can be 3-4× faster compared to chip-down designs.

- How do I scale compute performance over multiple product generations?

Choose a module family (SMARC, OSM, COM-HPC) with consistent pinouts and evolving silicon options. This allows CPU/NPU upgrades without redesigning the core board.

- How do I keep thermal throttling under control?

Select platforms validated for sustained performance, not peak frequency. Ensure thermal design matches the real workload duty cycle.

- When should I use an FPGA in my compute architecture?

Use FPGAs when you need deterministic latency, complex sensor timing or custom I/O pipelines. They excel where CPUs and GPUs introduce jitter.