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Acceleration device for deep learning and computing device

A deep learning and acceleration device technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as limited acceleration effects and inability to meet actual needs, and achieve reduced computing delays, data cache costs and instructions The effect of switching cost and improving data processing efficiency

Pending Publication Date: 2021-02-23
ALIBABA GRP HLDG LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The common hardware acceleration structure is based on SIMD (Single Instruction Multiple Data), so the acceleration effect on this iterative network is very limited. It takes tens to hundreds of seconds to generate a voice with a length of 1 second, which is completely unsatisfactory. The actual needs of real-time speech generation products

Method used

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  • Acceleration device for deep learning and computing device
  • Acceleration device for deep learning and computing device
  • Acceleration device for deep learning and computing device

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Embodiment Construction

[0072] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the application. However, the present application can be implemented in many other ways different from those described here, and those skilled in the art can make similar promotions without violating the connotation of the present application. Therefore, the present application is not limited by the specific implementation disclosed below.

[0073] In order to enable those skilled in the art to better understand the solution of the present application, a specific application scenario embodiment of the present application is firstly described in detail. Such as figure 1 As shown in , it is a schematic diagram of an embodiment of an application scenario of an acceleration device for deep learning provided by the present application. figure 1 The server in can be deployed in the cloud. The server uses the acceleration device for deep learning provided by ...

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Abstract

The invention discloses an acceleration device for deep learning and a computing device for deep learning. The acceleration device for deep learning comprises a data buffer and an operation module connected with the data buffer; the data buffer is used for caching an intermediate calculation result of a network layer of the vocoder neural network; and the operation module is used for obtaining anintermediate calculation result of a first network layer of the vocoder neural network from the data buffer according to the received control instruction, and sending the intermediate calculation result of the first network layer to the data buffer. By adopting the acceleration device for deep learning provided by the invention, the processing efficiency of the vocoder neural network is improved.

Description

technical field [0001] This application relates to the field of circuit design, in particular to an acceleration device and a computing device for deep learning. Background technique [0002] In the field of artificial intelligence applications, voice applications account for a considerable proportion. Text-to-speech (TTS, Text to speech) technology, as a voice application, is often used in human-computer interaction. Generally, the TTS algorithm can be further divided into three parts: the front-end for preprocessing text into linguistic features such as morphemes, the back-end for transforming linguistic features into acoustic features such as spectrum and phoneme, and the use of acoustic features The vocoder that generates the final speech. In these three parts, since the output frequency of the vocoder is equal to the sampling frequency of the audio signal, that is, 16-22kHz, the vocoder has a large amount of calculation and memory access, which has always been the bot...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/063G06N3/08
CPCG06N3/063G06N3/08G06N3/045
Inventor 林伟张健松夏立雪蒋昭梁昊
Owner ALIBABA GRP HLDG LTD
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