Editing Applications of Edge Computing (section)

===8.4.1 AI and Edge Processing===
As discussed in the previous sections, AUVs require specialized processing hardware that balances computational power, energy efficiency, and ruggedization for deep-sea environments. This section examines state-of-the-art approaches to implementing AI at the edge in underwater systems, covering hardware platforms, optimized algorithms, and emerging trends in the field.

* '''Hardware Solutions'''
# '''GPU-Powered Systems'''
:: Embedded GPU systems balance computational performance with power efficiency.
:: * NVIDIA Jetson AGX Orin (50-200 TOPS) for real-time sonar processing. It performs around 32-200 trillion operations per second (TOPS). In efficiency mode (15W), it can process sonar images at 5-10 frames per second (FPS).
:: * Qualcomm QCS6490 has integrated 5G capabilities. This enables AUVs operating near the surface to maintain communication while performing edge processing tasks.

# '''Field-Programmable Gate Arrays (FPGAs)'''
:: FPGAs can be reconfigured and can be easily adapted to different mission requirements. They combine AI engines with programmable logic, consuming only 8-15W while performing real-time object detection. 
:: * Use Case: The BlueROV2 platform uses Xilinx Versal ACAP for underwater infrastructure inspection with 95% defect detection accuracy. The Xilinx Versal ACAP runs low-power (8-15W) convolutional neural networks.
:: * Intel Cyclone V SoC is another such FPGA that consumes even less power (<5W). It is usually used for swarm AUV applications which require only basic edge processing. 
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* '''Software solutions: Optimized AI Models'''
# '''Model Optimization'''
:: There are several techniques to optimize factors such as memory usage, power consumption, and accuracy of AI models to fit the constraints of AUVs. Here are a few:

:: * Quantization, a technique that reduces numerical precision from 32-bit floating point to 8-bit integers. this leads to a 4x reduction in model size with minimal accuracy loss (<1%).
:: * Pruning is the process of removing redundant neural network connections. This eliminates unnecessary parameters while maintaining 95% performance again leading to a smaller model size.
:: * Knowledge Distillation is another powerful model size reduction technique where a large "teacher" model trains a smaller "student" model. The smaller model is then deployed reducing model complexity while preserving accuracy.

# ''' Model Architecture'''
:: Recent advances in neural network design have produced several architectures that can be particularly suited for underwater edge applications.

: Apart from model optimization and choosing the right architecture for specific use-cases, certain implementation techniques such as data pre-processing and selective transmission of processed results can be used improve the performance of these AI models. 

'''Case Study: MBARI's Benthic Observer'''

The Monterey Bay Aquarium Research Institute (MBARI) achieved a 70% reduction in AI power consumption by using some of the strategies discussed above. The Observer uses TensorRT-optimized models with dynamic voltage and frequency scaling and adaptive sampling strategies.