A data science team has developed a high-accuracy, 32-bit floating-point (FP32) convolutional neural network (CNN) for a complex object detection task. The business requires this model to be deployed on a fleet of battery-powered aerial drones with significant constraints on processing power, memory, and energy consumption for real-time inference. Which of the following strategies is the most effective for adapting the model for this edge computing scenario while attempting to minimize accuracy loss?
Applying post-training quantization to convert model weights to 8-bit integers (INT8) and using structured pruning to remove entire redundant filters.
Deploying the model to a high-performance cloud server and creating a REST API for the drones to send image data for remote inference.
Retraining the entire model from scratch using a higher learning rate and applying aggressive L2 regularization to reduce weight magnitudes.
Implementing data augmentation through image rotation and scaling, and increasing the inference batch size to improve throughput.
The correct answer involves combining post-training quantization with structured pruning. Post-training quantization, specifically converting weights from 32-bit floating-point (FP32) to 8-bit integers (INT8), reduces the model's size by approximately 75% and significantly speeds up inference, especially on hardware with specialized INT8 support. This directly addresses memory and energy consumption constraints. Structured pruning is a technique that removes entire filters or channels from the network, which is more hardware-friendly than unstructured pruning and leads to direct computational speedups by reducing the total number of operations. This combination provides a robust approach to making a large model viable for a resource-constrained edge device.
Deploying the model to a cloud server and using a REST API is the opposite of edge computing and would introduce unacceptable latency for a real-time task on a drone, which may also have inconsistent network connectivity.
Data augmentation and increasing batch size are techniques used during model training to improve robustness and training efficiency, respectively. Data augmentation does not optimize a pre-trained model for deployment, and increasing the batch size would increase, not decrease, the memory requirements for inference.
Retraining with a higher learning rate and L2 regularization are training-time adjustments. While L2 regularization can help reduce model complexity slightly, it is not a primary or sufficient optimization technique for the severe constraints of edge deployment compared to quantization and pruning.
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