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Programmable device-based convolutional neural network acceleration method and system

A convolutional neural network and device technology, applied in the field of convolutional neural network acceleration methods and systems, to achieve the effects of easy implementation, simple and flexible structure, and reduced complexity

Active Publication Date: 2017-11-24
INST OF COMPUTING TECH CHINESE ACAD OF SCI
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AI Technical Summary

Problems solved by technology

However, this assumed situation often has a large deviation from the actual situation, so there is room for further improvement in the actual frequency of the accelerator

Method used

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  • Programmable device-based convolutional neural network acceleration method and system
  • Programmable device-based convolutional neural network acceleration method and system
  • Programmable device-based convolutional neural network acceleration method and system

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Embodiment Construction

[0059] In order to make the above-mentioned features and effects of the present invention more clear and understandable, the following specific examples are given together with the accompanying drawings for detailed description as follows.

[0060] The invention belongs to a hardware realization design method of deep learning. Such as figure 1 As shown, the present invention provides a method for designing a convolutional neural network accelerator based on a programmable device, which can experimentally obtain the highest frequency that the programmable device can reach under the actual power supply voltage, temperature, and component technology level, and according to The highest frequency is used to effectively improve the performance of the convolutional neural network accelerator, which specifically includes the following steps:

[0061] Step S1, design the basic structure of the convolutional neural network on the programmable device, and according to the computing reso...

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Abstract

The present invention relates to a programmable device-based convolutional neural network acceleration method and system. The method comprises: designing a basic structure of a convolution neural network on the programmable device, and establishing the quantification model of the computing resource and the frequency about the parallelization parameter respectively; under different parallelization parameters, exploring the highest reachable clock frequency of the actual voltage, temperature, process deviation, establishing an analysis model of the actual highest reachable frequency and the parallelization parameter; and taking throughput calculation as an optimization purpose, according to the established quantitative model and analysis model, carrying out problem abstract on the convolutional neural network design space exploration, and using a certain search algorithm to solve the parallelization parameters with optimal performance. According to the method and system provided by the present invention, the delay margin reserved by the commercial design tools for the voltage, the temperature and the process deviation can be used while ensuring the stability and reliability of the accelerator, so that the performance of the convolutional neural network accelerator can be further improved.

Description

technical field [0001] The present invention relates to the field of integrated circuits and the field of deep learning, in particular to a convolutional neural network acceleration method and system based on programmable devices. Background technique [0002] Convolutional neural network is a multi-layer perceptron with good fault tolerance, parallel processing ability and self-learning ability. It has good robustness and computational efficiency in dealing with graphics problems, especially in identifying displacement, scaling and other forms of distortion invariance, so it is widely used in deep learning as a benchmark neural network architecture. [0003] Field Programmable Gate Array (Field Programmable GateArray, FPGA) is a programmable device, which has the advantages of abundant computing resources, flexible reconfiguration, short development cycle and low power consumption. Compared with CPU (Central Processing Unit) and GPU (Graphics Processing Unit), Field Progra...

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Application Information

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IPC IPC(8): G06N3/063
CPCG06N3/063
Inventor 陆维娜卢文岩叶靖胡瑜李晓维
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI
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