Machine learning system and method and apparatus for creating machine learning system
A machine learning and filter coefficient technology, applied in machine learning, neural learning methods, instruments, etc., can solve the problem that RNN cannot be strongly quantized, and achieve the effect of saving energy and reducing filter coefficients
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[0050] figure 1 A schematic diagram of a diagram is shown in which the measured energy consumption E of a neural network (NN-temp) with temporal filtering and the measured energy consumption E of a RNN are plotted against the quantization resolution Q. Note that both networks achieve similar results on the same dataset. The y-axis illustrates energy consumption E, which is lowest at the origin of the graph and increases with distance from the origin. The quantization resolution Q is plotted on the x-axis, increasing towards the origin of the graph. That is, the closer to the origin, the higher the quantization resolution, especially the number of bits used to represent the parameters. In the following, an exemplary case is considered: a neural network with temporal filtering (NN-temp) has a significantly greater energy consumption for high quantization resolutions than a similar RNN at the same quantization resolution.
[0051] If the resolution of the parameters of the two...
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