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Character recognition method of convolutional neural network based on RISC-V

A convolutional neural network, RISC-V technology, applied in neural learning methods, biological neural network models, character recognition, etc., can solve problems such as poor design and implementation

Active Publication Date: 2020-08-07
HANGZHOU DIANZI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the research on RISC-V acceleration instructions is not well designed and implemented

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  • Character recognition method of convolutional neural network based on RISC-V
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  • Character recognition method of convolutional neural network based on RISC-V

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Embodiment Construction

[0021] The present invention will be further limited below in conjunction with the accompanying drawings and embodiments, but not limited thereto.

[0022] Such as figure 1 , shown in 2, the application method of RISC-V self-defined acceleration instruction design in the convolutional neural network of the present invention specifically includes the following steps:

[0023] S1. The RISC-V instruction set architecture is a flexible and scalable architecture. The scalability of RISC-V is mainly reflected in two aspects: 1. Predefined Custom instructions, 2. Reserved instruction encoding space. This design uses the reserved instruction encoding space to realize the accelerated instruction.

[0024] The instruction set defined by the RISC-V architecture uses only a small portion of the instruction encoding space. More instruction encoding spaces are reserved for users to use as extended instructions. Since the RISC-V architecture supports a variety of different instruction le...

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Abstract

The invention discloses a character recognition method of a convolutional neural network based on RISC-V. The method includes: firstly building a neural network training environment, and then designing a six-layer handwritten number recognition network based on an mnist data set; inputting a picture, performing training to set accuracy, and storing a weight and offset parameter file; setting a custom instruction type and format of the RISC-V instruction set, designing an instruction according to a reserved instruction coding space, and designing a specific hardware acceleration circuit according to the set instruction format and an SIGMOD function implementation method; importing a hardware acceleration circuit, a weight and a bias parameter file into the FPGA, building an RISC-V development environment, and completing the addition of a custom instruction; and inputting the picture, and identifying the numbers in the picture by using a hardware acceleration circuit. According to the invention, instruction-level acceleration of specific operation is realized, partial neural network reasoning operation is accelerated, and handwritten numbers in a picture can be quickly identified.

Description

technical field [0001] The invention belongs to the field of instruction acceleration for special processors, in particular to a volume based on RISC-V acceleration instructions [0002] A text recognition method based on a neural network. Background technique [0003] At present, image text recognition through neural networks is relatively inefficient, and the RISC-V instruction set is an open instruction set architecture (ISA) based on the principle of reduced instruction set computing (RISC). RISC-V is based on the continuous development and maturity of the instruction set. A new command built on the basis of The RISC-V instruction set is completely open source, simple in design, easy to port to Unix systems, modular design, complete tool chain, and there are a large number of open source implementations. However, the research on RISC-V acceleration instructions is not well designed and implemented. Contents of the invention [0004] Aiming at the deficiencies of the...

Claims

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Application Information

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IPC IPC(8): G06K9/32G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/62G06V30/10G06N3/045G06F18/241
Inventor 秦兴周志远
Owner HANGZHOU DIANZI UNIV
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