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Adaptive Sparsity for Efficient and Reliable Machine Learning Inference in Autonomous Vehicles
Autonomous vehicles rely on machine learning models that must make accurate predictions under strict latency, memory, energy, and reliability constraints. However, modern deep learning systems are often designed as fixed, dense inference pipelines, even though real driving conditions vary across com
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