Implementing deep learning on edge devices holds immense potential for revolutionizing defense applications by enabling real-time intelligence, enhanced situational awareness, and decentralized decision-making capabilities.
However, there are challenges to overcome in implementing deep learning on edge devices. Limited computational resources and power constraints pose challenges for running complex deep learning models in real-time on resource-constrained devices.
In this case study, learn from leading defense companies how you can boost your models’ performance and maximize hardware utilization to deliver accurate and cost-efficient inference on cloud or edge devices.
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