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DNN Quantization: Theory to Practice - 2024 Summit

Deep neural networks, widely used in computer vision tasks, require substantial computation and memory resources, making it challenging to run these models on resource-constrained devices. Quantization involves modifying DNNs to use smaller data types (e.g., switching from 32-bit floating-point values […]



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DNN Quantization: Theory to Practice - 2024 Summit

https://embeddedvisionsummit.com/2024/session/dnn-quantization-theory-to-practice

Deep neural networks, widely used in computer vision tasks, require substantial computation and memory resources, making it challenging to run these models on resource-constrained devices. Quantization involves modifying DNNs to use smaller data types (e.g., switching from 32-bit floating-point values […]



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https://embeddedvisionsummit.com/2024/session/dnn-quantization-theory-to-practice

DNN Quantization: Theory to Practice - 2024 Summit

Deep neural networks, widely used in computer vision tasks, require substantial computation and memory resources, making it challenging to run these models on resource-constrained devices. Quantization involves modifying DNNs to use smaller data types (e.g., switching from 32-bit floating-point values […]

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