Greater Computational Power vs Lower Energy Consumption
Map computational workload distribution decisions to the AI Act's robustness requirements and document energy-performance trade-offs in the technical file.
CyberTRIZ analysis · AIRobotics contradiction AS017 · one of 8,235 worked contradictions published by CyberTRIZ.AI
Regulations
Business Context
Autonomous platforms increasingly depend on AI computing for perception, planning, and control while operating under limited battery or energy resources.
AI & Robotics TRIZ Resolution
Distribute computational workloads between onboard systems, edge infrastructure, and cloud resources according to mission requirements.
Applicable TRIZ Principles
Principle 2 – Taking Out offloads non-critical processing from energy-constrained platforms.
Principle 19 – Periodic Action schedules intensive processing only when operationally required.
Principle 28 – Mechanics Substitution replaces hardware-intensive processing with more efficient digital alternatives.
Expected Outcome
Higher computational capability
Lower energy consumption
Extended mission duration
Better resource utilization
Decision Indicators
Early indicators include:
Battery consumption increases rapidly.
Processor utilization remains near capacity.
Mission duration decreases.
Thermal management becomes difficult.
Monitoring these indicators improves energy efficiency.