• What is the difference between TDP and actual power consumption?

Thermal Design Power (TDP) is a theoretical value representing the maximum heat a cooling system must dissipate under a maximum theoretical load. Actual power consumption is dynamic; it varies based on your neural network’s complexity, the precision used (e.g., INT8 vs. FP32), and how often the hardware accelerators are active. In robotics, actual consumption is often lower than TDP, but "spikes" during heavy inference can trigger thermal throttling.

• How does INT8 quantization save power?

Quantization reduces the precision of weights and activations from 32-bit (FP32) to 8-bit (INT8). This saves power in two ways:

- Reduced Data Movement: Moving 8-bit data across the chip consumes significantly less energy than moving 32-bit data.

- Efficient Math: Dedicated Integer units (Tensor Cores) are physically smaller and require fewer transistors to perform multiplications, leading to higher TOPS/Watt.

• Why does my robot run slower as the battery drains?

Most Edge AI platforms use Dynamic Voltage and Frequency Scaling (DVFS). As the battery voltage drops or heat builds up, the system automatically lowers the clock frequency to maintain stability. This reduces power draw but increases latency. Proper thermal management (heat sinks/airflow) is critical to keeping the clock speeds high.

• Can software choice really impact battery life?

Yes. Using highly optimized runtimes like NVIDIA TensorRT or hailo-rt ensures the AI workload is mapped efficiently to the hardware's specialized cores. Unoptimized code often defaults to the CPU, which is far less efficient than a GPU or NPU, leading to a much lower TOPS/Watt ratio and faster battery depletion.

• What is "Power Gating" in robotics?

Power gating is a hardware-level strategy where specific parts of the chip (like the GPU or an AI engine) are completely shut off when not in use. For a robot that only runs inference every few seconds, ensuring your software supports these "sleep modes" can extend battery life by up to 30%.

• Is it better to have more TOPS or a lower Wattage?

Neither—focus on the ratio. A 100 TOPS chip that pulls 50W (2 TOPS/W) will drain your battery faster and generate more heat than a 20 TOPS chip that pulls only 5W (4 TOPS/W). For Edge AI, the most efficient chip is the one that meets your latency requirements at the lowest power draw.

• What is the "Dark Silicon" problem in Edge AI?

"Dark Silicon" refers to the phenomenon where a significant portion of a chip cannot be powered on simultaneously due to thermal constraints, even if the gates are physically present. In robotics, this means that even if a chip claims high TOPS, the thermal envelope of a fanless robot might only allow $20\%$ to $30\%$ of those transistors to be active at once. Strategy involves spatial multiplexing, where different accelerators are turned on and off in sequence rather than all at once.

• How does memory bandwidth affect the energy budget?

In many AI workloads, the energy cost of moving data from external DRAM to the processor is $10\text{x}$ to $100\text{x}$ higher than the energy required for the actual mathematical calculation. To optimize power, engineers should prioritize hardware with large on-chip SRAM caches and software that supports tiling/fusing, which keeps data local to the processor for as long as possible.

• What is "Opportunistic Power Management"?

This is an advanced strategy where the robot's AI workload is dynamically adjusted based on energy availability. For example, if a solar-powered robot is in direct sunlight or a drone is gliding (recapturing kinetic energy), the system may switch from a "lightweight" INT4 model to a "high-accuracy" FP16 model. When energy levels are low, the system reverts to a lower-power state to preserve mission longevity.