Why this decision matters
Embedded AI should define minimum performance and stability gates before selecting models, chips, systems and optimization depth.
Prioritize stable operation in the target scenario, then optimize latency, throughput, power and cost. Regression is required after every optimization.
Conditions to confirm before development
- Define end-to-end latency, throughput, power, thermal and memory limits
- Capture real camera, sensor, network and storage conditions
- Compare model quality against quantization, pruning and acceleration
- Test power loss, network loss, abnormal input, endurance and recovery
Implementation and delivery approach
Create a stable baseline on the target board and optimize one layer at a time so performance changes and regressions remain traceable.
Record hardware, system image, model, configuration, ambient conditions, input and test duration for reproducible results.
Acceptance boundary
A development-board result does not represent the final product. Cooling, power, peripherals and field data can change behavior.