How do you optimize computational efficiency in autonomous vehicle systems to reduce energy consumption?
Instruction: An original compute run uses an average total-system power of 100 W for 10 seconds. A proposed optimization uses 80 W for 15 seconds on the same task and measurement boundary. Calculate energy and evaluate the claim that lower power proves better energy efficiency. Then outline the additional workload checks.
Assume the supplied powers are time averages over each complete run and both perform the same useful task. Include all relevant system components within the same stated boundary; no vehicle-range claim follows from this isolated example.
Updated
Worked answer
Energy is average power multiplied by elapsed time. The original run consumes 100×10=1,000 J. The proposed run consumes 80×15=1,200 J:20% more energy despite 20% lower average power. If both deliver the same useful output under the stated assumptions, the proposed run is worse on energy per task and is also slower.
Profile the actual bottleneck before selecting an algorithm, accelerator or compressed model. Lower asymptotic complexity does not guarantee lower energy for a particular size and implementation. Include data movement, synchronization, memory, cooling and startup as appropriate; compare the same complete measurement boundary.
Pruning needs representations and kernels that exploit the removed work. Quantization changes supported parameters or activations and may introduce conversion or quality costs. Distillation can reduce model size without guaranteeing the teacher's quality. Test relevant downstream quality, timing and thermal behavior after each change.
Measure representative workloads repeatedly with documented hardware/software configuration and uncertainty. Compare power, elapsed time and energy from the same run instead of combining the best measurements from different runs. Whole-vehicle energy and service outcomes need a broader evaluation.
Source: MLPerf system-power measurement boundaries. This arithmetic is a standalone exercise, not a claim of MLPerf compliance.