FPGA, NPU or MPU? Partitioning an Edge AI Architecture
A practical guide to partitioning edge AI workloads across FPGA, NPU, MPU and MCU resources by latency, power and software needs.
Read the note →A configurable inertial processing module that combines motion sensing, time synchronization and embedded fusion for navigation, stabilization and platform-state estimation.
Navigation performance depends on the entire chain: sensor selection, mechanical placement, temperature behavior, timing, calibration and the fusion model. Treating the IMU as an isolated component usually leaves avoidable errors at system level.
The Alvexis approach integrates inertial sensing with embedded processing and platform interfaces. The module can serve as a navigation aid, motion-reference source or synchronized input to a wider perception stack.
IMU and optional magnetic, pressure or wheel/odometry inputs.
Hardware timestamps and synchronization to platform clocks.
Attitude, velocity or navigation-state estimation with optional GNSS aiding.
CAN, serial or Ethernet output with health and status data.
Values depend on the selected sensors, processor, interfaces, environment and acceptance method.
| Area | Definition |
|---|---|
| Performance class | Determined by sensor grade, aiding sources and environmental profile. |
| Update rate | Selected from platform dynamics and interface constraints. |
| Calibration | Factory and field calibration concepts are defined with the mechanical installation. |
| Program status | Roadmap module; final sensor set and claims require application validation. |
Send the interfaces, environment, decision metric and pilot quantity. We will identify the architecture and validation plan.