Accelerator of pulmonary-nodule detection neural-network and control method thereof

Abstract

The invention provides an accelerator of a pulmonary-nodule detection neural-network and a control method thereof. Input data enter an FIFO module through a control module, then enter a convolution module to complete multiplication and accumulation operations in convolutions, and enter an accumulation module after the multiplication and accumulation operations to accumulate intermediate values; the intermediate values after accumulation enter an activation function module for activation function operations, enter a down-sampling module after the activation function operations for mean pooling,and enter a rasterization module after mean pooling for rasterization; output is converted to a one-dimensional vector, and is returned to the control module; and the control module calls and configures the FIFO module, the convolution module, the accumulation module, the activation function module, the down-sampling module and the rasterization module to control iteration, and transmits iteration results to a fully connected layer for multiplication and accumulation operations and probability comparison. According to the accelerator, iteration control logic is optimized for the pulmonary-nodule detection network through the control module to reduce resource consumption, and a data throughput rate is increased.

Description

technical field [0001] The invention relates to the technical field of neural networks, in particular to a neural network accelerator for lung nodule detection and a control method thereof. Background technique [0002] The neural network has high precision and strong learning ability, and has extensive and important applications in image recognition, pattern recognition and other fields. There are many types of neural networks, including BP neural network, convolutional neural network (CNN), and recurrent neural network (RNN). Among them, the convolutional neural network plays an important role in the field of image recognition due to its characteristics of weight sharing and extraction of regional features. In the Large Scale Vision Challenge (ILSVRC), the best results in image recognition were created by convolutional neural network-related algorithms. [0003] Pulmonary nodule detection is an important means of diagnosing early lung cancer. At this stage, most of them ...

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Problems solved by technology

In this way, the parallel operation of the fully connected layer is difficult to be developed, and the data transmission between the convolutional layer and the fully connected layer will cause performance degradation

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