10 interview questions and answers on Edge AI Hardware

Here’s a list of questions and answers about the Edge AI Hardware. This guide can help you prepare for job interviews for Edge AI skill requirements.

List of 10 Edge AI Questions and Answers

Question 1: What is Edge AI?

Answer 1: Edge AI refers to running AI models locally on edge devices rather than relying on cloud servers.

Question 2: Name two Edge AI hardware platforms.

Answer 2: NVIDIA Jetson Nano and Google Coral Dev Board.

Question 3: Why is Edge AI important?

Answer 3: It offers low latency, enhanced privacy, offline operation and reduced bandwidth use.

Question 4: What is a VPU?

Answer 4: Vision Processing Unit – designed for high speed image/video processing on edge devices.

Question 5: What does the Movidius chip specialize in?

Answer 5: Efficient inference of deep learning models on edge with low power.

Question 6: What is the role of TPUs in Edge AI?

Answer 6: Tensor Processing Units accelerate matrix operations for neural networks, enhancing AI performance on the edge.

Question 7: How does edge inference differ from cloud inference?

Answer 7: Edge inference happens locally, while cloud inference requires internet and remote servers.

Question 8: Mention one challenge of Edge AI hardware.

Answer 8: Limited computational and memory resources compared to data centers.

Question 9: What is quantization in Edge AI models?

Answer 9: Reducing model size and precision (e.g., float32 to int8) for faster inference and less memory usage.

Question 10: What is the main advantage of using an NPU (Neural Processing Unit)?

Answer 10: NPUs are optimized for AI workloads with high parallelism and energy efficiency.