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 of the following challenges of edge deployments is explicitly mentioned in the passage?
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 of the following challenges of edge deployments is explicitly mentioned in the passage?
Options:
- Stringent power and thermal constraints
- High cost of cloud bandwidth
- Lack of edge microcontrollers
- Inability to train deep learning models
- कड़ी ऊर्जा (पावर) और ताप (थर्मल) संबंधी बाधाएं
- क्लाउड बैंडविड्थ की उच्च लागत
- एज माइक्रोकंट्रोलर्स की अनुपलब्धता
- डीप लर्निंग मॉडल को प्रशिक्षित करने में असमर्थता
Correct Answer: a)
Explanation: