Question:
Directions (Q. 16 – 20): Read the following passage carefully and answer the question that follows:
Artificial Intelligence in edge computing has drastically transformed real-time decision-making in autonomous systems. By shifting computation from remote cloud servers to local gateways and edge microcontrollers, latency is minimized to microseconds. However, edge deployments introduce stringent power and thermal constraints. Engineers must optimize neural network architectures through techniques like post-training quantization, pruning, and knowledge distillation, without incurring catastrophic degradation in classification accuracy.
Which technique is NOT mentioned in the passage for optimizing neural networks for edge devices?
Directions (Q. 16 – 20): Read the following passage carefully and answer the question that follows:
Artificial Intelligence in edge computing has drastically transformed real-time decision-making in autonomous systems. By shifting computation from remote cloud servers to local gateways and edge microcontrollers, latency is minimized to microseconds. However, edge deployments introduce stringent power and thermal constraints. Engineers must optimize neural network architectures through techniques like post-training quantization, pruning, and knowledge distillation, without incurring catastrophic degradation in classification accuracy.
Which technique is NOT mentioned in the passage for optimizing neural networks for edge devices?
Options:
- Reinforcement fine-tuning from human feedback (RLHF)
- Post-training quantization
- Pruning
- Knowledge distillation
- Reinforcement fine-tuning from human feedback (RLHF)
- Post-training quantization (क्वांटाइजेशन)
- Pruning (प्रूनिंग)
- Knowledge distillation (नॉलेज डिस्टिलेशन)
Correct Answer: a)
Explanation: