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.
According to the passage, why is computation shifted from the cloud to the edge?
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.
According to the passage, why is computation shifted from the cloud to the edge?
Options:
- To minimize latency to microseconds for real-time decision-making
- To eliminate the need for microcontrollers
- To maximize thermal dissipation in cloud centers
- To avoid using neural network optimization techniques
- वास्तविक समय के निर्णयों के लिए विलंबता (latency) को माइक्रोसेकंड तक न्यूनतम करने हेतु
- माइक्रोकंट्रोलर की आवश्यकता को समाप्त करने हेतु
- क्लाउड केंद्रों में थर्मल डिसिपेशन को अधिकतम करने हेतु
- न्यूरल नेटवर्क अनुकूलन तकनीकों से बचने हेतु
Correct Answer: a)
Explanation: