Dec 1, 2023 · the intel® fpga ai suite supports running inference on int8 symmetrically quantized cnns that are represented in the openvino™ intermediate representation (ir). Efficient acceleration of deep convolutional neural networks is currently a major focus in edge computing research. 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. Feb 1, 2021 · when deploying resnet18 on the given two fpga devices, we obtain comparable accuracy while reducing latency by 1. 32× and 1. 24× compared to the mix&match design [3].
Intel® fpga ai suite inference on quantized graphs Quantization compresses the parameters in a neural network by reducing the number of bits used to represent them. This in turn reduces both the size of each calculation and the time and.
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