Parallel optimization method of convolutional neural network
A convolutional neural network and optimization method technology, applied to biological neural network models, neural architectures, etc., can solve the problems of no convolution algorithm optimization, low parallel efficiency of convolutional neural network models, and waste of processing power. Achieve the effect of reducing time complexity, reducing computing overhead, and reducing complexity
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[0031] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary only, and are not intended to limit the scope of the present invention. Also, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concept of the present invention.
[0032] The parallel optimization method of the convolutional neural network of the present invention comprises:
[0033] Step 1: Replace the winograd algorithm f(4x4,3x3) with f(2x2,3x3) to reduce the time complexity and the number of multiplication operations.
[0034] Step 2: For the for loop structure part that does not have loop data dependence, use OpenMP to open up multiple threads for calculation, so as to achieve...
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