The pr strategy is based on. 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. Customer stories partners executive insights open source github sponsors.
Chengyih001 / resnet18_fpga_accelerator public. Quantization, the process of reducing the neural network parameters into lower precision, optimizes models for the limited computational resources of fpgas. Models like lenet and. This is my final year thesis supervised under professor wei zhang, hkust. More details regarding this thesis can be found in the research report under hardware acceleration for ai. Source the python virtual.
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