Method, storage device and terminal for neural network compression and acceleration
A neural network and feature map technology, applied in the computer field, can solve problems such as high computing cost and inability to effectively reduce neural network computing resources and storage space, and achieve the effect of saving computing resources, reducing computing costs, and good acceleration effects
Active Publication Date: 2021-09-17
广州方硅信息技术有限公司
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Problems solved by technology
[0005] Aiming at the shortcomings of the existing methods, the present invention proposes a neural network compression and acceleration method, storage device and terminal to solve the problems in the prior art that the calculation resources and storage space of the neural network cannot be effectively reduced, and the calculation cost is relatively high problems to reduce neural network computing resources and storage space, thereby reducing computing costs
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[0053] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.
[0054] Those skilled in the art will understand that unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the word "comprising" used in the description of the present invention refers to the presence of said features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, Integers, steps, operations, elements, components, and / or groups thereof.
[0055] Those skil...
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The present invention provides a neural network compression and acceleration method, storage device and terminal. The method includes the steps of: pruning the original neural network; clustering and quantifying the network weights of the pruned original neural network; The original neural network after clustering and quantization is trained to obtain the target neural network; the target neural network is stored using a sparse matrix; the input feature map is converted into an input matrix; the sparse matrix is multiplied by the input matrix to obtain the The output feature map corresponding to the above input feature map. This embodiment reduces neural network computing resources and storage space, thereby reducing computing costs.
Description
technical field [0001] The present invention relates to the field of computer technology, in particular, the present invention relates to a neural network compression and acceleration method, storage device and terminal. Background technique [0002] With the development of neural network models, in order to solve increasingly difficult problems such as classification, recognition and detection, deeper and larger neural network models have begun to be applied to such problems. For example, for deep learning algorithms that are currently widely used in artificial intelligence, the deep network structure is deep, and its calculation amount and model are large, so more computing resources and storage space are required. However, in production applications, server computing resources are becoming more and more scarce, the speed requirements are getting higher and higher, and the demand for porting to mobile terminals is becoming more and more urgent. Compression and test accele...
Claims
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IPC IPC(8): G06N3/08G06T1/20G06K9/62
CPCG06N3/082G06T1/20G06F18/2136G06F18/23213G06F18/24
Inventor 杨达坤曾葆明
Owner 广州方硅信息技术有限公司