Feedforward neural network hardware realization method based on multicore technology

A technology of feedforward neural network and hardware implementation, applied in the direction of biological neural network model, physical implementation, etc., can solve the problems of large circuit scale, high cost, and inability to realize neural network simulation, etc., and achieve the effect of low system cost and simple structure

Inactive Publication Date: 2010-02-03
王连明
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Problems solved by technology

[0002] At present, the application method of artificial neural network is mainly based on the software simulation of general-purpose computer. Although this implementation method has the advantages of flexible application and no need for special hardware, the main problem is: because the general-purpose computer runs the program according to the order of instructions, so , cannot truly simulate the characteristics of high-speed, distributed, and parallel computing of biological neural networks
However, the imp

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  • Feedforward neural network hardware realization method based on multicore technology
  • Feedforward neural network hardware realization method based on multicore technology
  • Feedforward neural network hardware realization method based on multicore technology

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Embodiment Construction

[0015] Such as figure 1 As shown, one core is used as the main control core, and the main control core is responsible for managing the memory space of the adjacency table. Each neuron corresponds to a fixed continuous space in the memory space of the adjacency list, of which one part is the weight space, which is used to store its own weight, and the other part is the instruction space, which is used to store the instructions of the main control core. According to the requirements of the network structure, the main control core stores the storage address of the input weight of each neuron in the instruction space of the corresponding neuron in the form of instructions, and each neuron only needs to read the input weight according to the instruction to calculate You don't need to care about the network structure. Using this structure, it is also possible to modify the form of the activation function used by each neuron to calculate, thereby forming a more complex neural networ...

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Abstract

The invention discloses a feedforward neural network hardware realization method based on multicore technology, relating to a computer system based on a specific calculation model. The invention adopts an NIOS II soft core processor and utilizes Quartus II software in an FPGA development system thereof to build a multicore processor system, the number of processor cores is only limited by chip scale, each processor core can be independently programmed, and multiple processor cores can simultaneously run respective program and simulate the characteristics of nerve cells of any kind by performing programming of one single core; in the network studying process, the single cores calculate output amount in a parallel mode and finish the adjustment of corresponding weights and thresholds; the input and output of each single core are performed with data exchange via a multiport memory to stimulate feedforward neural network with any structure, which realizes the purposes of high speed, distribution and parallel calculation of the neural network.

Description

technical field [0001] The invention relates to a computer system based on a specific calculation model, in particular to a hardware implementation method of a feedforward neural network based on multi-core technology. Background technique [0002] At present, the application method of artificial neural network is mainly based on the software simulation of general-purpose computer. Although this implementation method has the advantages of flexible application and no need for special hardware, the main problem is: because the general-purpose computer runs the program according to the order of instructions, so , it is impossible to truly simulate the characteristics of high-speed, distributed, and parallel computing of biological neural networks. However, the implementation methods based on pure hardware, such as the implementation methods using analog circuits, digital circuits or hybrid circuits, can only simulate specific networks, and it is difficult to simulate the weight...

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Application Information

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IPC IPC(8): G06N3/06
Inventor 王连明张文娟
Owner 王连明
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