ResNet18's Improved Accuracy: FPGA And Quantization

ResNet18's Improved Accuracy: FPGA And Quantization

Nov 3, 2024 · quantization and pruning: Quantization reduces the model size by using lower precision (e. g. , converting fp32 to int8), which speeds up inference and saves memory. Jun 6, 2024 · in this paper, we improved the logarithmic quantization of weights and activations while also possessing nonmultiplicative computation capabilities with negligible loss in. Efficient acceleration of deep convolutional neural networks is currently a major focus in edge computing research.

Nov 3, 2024 · quantization and pruning: Quantization reduces the model size by using lower precision (e. g. , converting fp32 to int8), which speeds up inference and saves memory. Jun 6, 2024 · in this paper, we improved the logarithmic quantization of weights and activations while also possessing nonmultiplicative computation capabilities with negligible loss in. Efficient acceleration of deep convolutional neural networks is currently a major focus in edge computing research.

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