Convolutional neural network optimization method and device based on pulse array
A convolutional neural network and array technology, applied in the field of convolutional neural network optimization, can solve problems such as strict consistency, physical parameters of PE cannot be guaranteed, and the highest operating frequency cannot be strictly consistent.
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[0041] In order to facilitate the understanding of the solutions provided by the embodiments of the present invention, CNN and pulse arrays are introduced first.
[0042] CNN usually consists of multiple convolutional layers (Convolutional Layer, Conv Layer). The two operational elements of convolution include input activation (InputActivation) matrix or input feature map (InputFeatureMap), and filter (Filter).
[0043] Input the activation matrix to preprocess the image data to form a matrix that conforms to the size of the CNN model; the filter is another set of matrices (called filter matrices) obtained through CNN model training, and the elements stored in the filter matrix It is called Weight.
[0044] The convolution operation performed by each convolutional layer consists of scanning over the input activation matrix with a filter. Among them, every time the filter scans to a position of the input activation matrix, it uses its weight to perform a matrix dot multiplicat...
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