Three Technical Principles for Selecting the Right Platform

1. Align Architecture With the Dominant Workload

Robotics systems rarely run a single type of workload. The key to platform selection is identifying which workload dominates system behaviour – and choosing the compute architecture that executes it most efficiently and predictably.

Vision and perception workloads

Vision pipelines and AI-based perception are best handled by GPUs or NPUs, but for different reasons.

- GPUs offer high flexibility and are well suited for complex vision stacks, image processing and mixed workloads where graphics, compute and AI inference overlap.

- NPUs, by contrast, are optimised specifically for neural-network inference and deliver significantly higher efficiency in terms of TOPS per watt.

For systems with continuous inference under strict power budgets, NPUs are usually the better fit; for rapidly evolving models or heterogeneous vision tasks, GPUs provide greater programmability.

Deterministic control and high-speed data paths Applications requiring hard real-time behaviour, ultra-low latency or deterministic I/O –such as motor control, time-critical sensor fusion or custom high-speed interfaces – are best served by FPGAs. Their parallel architecture and hardware-level determinism make them ideal for latency-sensitive pipelines and high-bandwidth data handling, including pre-processing for AI workloads

Modern real-time MPUs increasingly integrate real-time cores, time-sensitive networking and low-latency peripherals. They can reliably handle soft to medium real-time tasks and simplify system design, but they remain limited when absolute determinism or extreme throughput is required

General system orchestration and application logic CPUs and MPUs form the backbone of robotics platforms. They manage operating systems, middleware, scheduling, communication stacks and overall system coordination. While they can execute AI workloads at low duty cycles, their primary role is to orchestrate data flow between accelerators, manage safety mechanisms and provide stable, predictable system behaviour. In most robotics systems, the MPU is the architectural anchor that ties together GPU, NPU and FPGA resources.

Choosing a single architecture to cover all these workloads typically leads to inefficiencies. Robust robotics platforms instead combine multiple compute blocks – each selected for the workload it handles best.

2. Use Metrics That Match the Architecture

• TOPS quantify neural inference efficiency (NPU)

• Cores/Frequency define CPU/MPU computational capacity

• Latency determines end-to-end responsiveness

Benchmarking architectures against mismatched metrics leads to misleading conclusions.

3. Validate Sustained Behaviour, Not Peak Specs

Real-time robotics workloads run continuously. Sustained throughput, stable frequency and predictable power draw are far more important than peak numbers on a datasheet.