Acceleration device and method for deep learning service

A deep learning and acceleration device technology, applied in neural learning methods, biological neural network models, etc., can solve problems such as limited computing components, difficulty in fully utilizing CPU resources, and cost increase, and achieve the effect of optimal performance and power consumption ratio

Active Publication Date: 2016-11-23
IFLYTEK CO LTD
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Simply increasing the CPU to improve the concurrent computing capability will linearly increase the number of CPUs as the business grows, and the cost will also increase linearly. Moreover, the CPU has limited computing components for user programs. If it is used for deep learning computing, Its CPU resources are dif...

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  • Acceleration device and method for deep learning service
  • Acceleration device and method for deep learning service
  • Acceleration device and method for deep learning service

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Embodiment Construction

[0024] In order to enable those skilled in the art to better understand the solutions of the embodiments of the present invention, the embodiments of the present invention will be further described in detail below in conjunction with the drawings and implementations.

[0025] In order to facilitate the understanding of the solution of the present invention, the following takes DNN (Deep Neural Network) as an example to briefly describe the calculation process of the deep learning algorithm.

[0026] like figure 1 As shown, DNN consists of an input layer, multiple hidden layers (6 hidden layers shown in the figure) and an output layer. It is a fully connected network. The connection weight between the input layer and the first hidden layer is D*H The weights between the hidden layers are H*H, and the weights between the sixth hidden layer and the output layer are H*M. In addition, each node of the hidden layer and output layer is also attached with a corresponding bias.

[00...

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Abstract

The invention discloses an acceleration device for the deep learning service, wherein the device is used for the deep learning calculation on to-be-processed data in a server. The device comprises a network card arranged at a server side, a calculation control module connected with the server through a bus, a first memory and a second memory. The calculation control module is a programmable logic device and comprises a control unit, a data storage unit, a logic storage unit, a bus interface, a first communication interface and a second communication interface, wherein the bus interface, the first communication interface and the second communication interface are respectively communicated with the network card, the first memory and the second memory. The logic storage unit is used for storing the depth learning control logic. The first memory is used for storing the weight data and the offset data of each layer of the network. Based on the device, the calculation efficiency can be effectively improved, and the performance power consumption ratio can also be improved.

Description

technical field [0001] The invention relates to the field of circuit design, in particular to an acceleration device and method for deep learning services. Background technique [0002] With the large-scale successful application of deep learning algorithms in the fields of speech recognition, image recognition, and natural language understanding, the number and frequency of users using deep learning-related services is gradually increasing; Concurrent computing capabilities for related business responses. There are two main methods to improve concurrent computing capabilities: one is to simply increase the CPU to increase concurrent computing capabilities; the other is to use a CPU+GPU heterogeneous system to increase concurrent computing capabilities. Simply increasing the CPU to improve the concurrent computing capability will linearly increase the number of CPUs as the business grows, and the cost will also increase linearly. Moreover, the CPU has limited computing comp...

Claims

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Application Information

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IPC IPC(8): G06N3/08
CPCG06N3/08
Inventor 张致江王智国于振华胡郁刘庆峰
Owner IFLYTEK CO LTD
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