Pulse neural network training method, storage medium, chip and electronic product

A pulse neural network and training method technology, applied in the direction of neural learning methods, biological neural network models, neural architecture, etc., can solve problems such as unsolvable algorithms and hardware, inability to fit or overfit, insufficient data volume, etc., to achieve Solve the problem of static visual blackout, stable performance, and overcome the effect of mismatch

Active Publication Date: 2022-04-29
SHENZHEN SYNSENSE TECH CO LTD
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Problems solved by technology

[0012] Although data enhancement technology can increase the diversity of data and solve the problem of unfitting or overfitting caused b

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  • Pulse neural network training method, storage medium, chip and electronic product
  • Pulse neural network training method, storage medium, chip and electronic product
  • Pulse neural network training method, storage medium, chip and electronic product

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Embodiment Construction

[0034] Since various alternative solutions cannot be exhaustively described, the main points of the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. For other technical solutions and details not disclosed in detail below, they generally belong to the technical objectives or technical features that can be achieved by conventional means in this field. Due to space limitations, the present invention does not introduce them in detail.

[0035]Unless it is the meaning of division, " / " at any position in the present invention means logical "or". The "first", "second" and other serial numbers in any position of the present invention are only used for distinguishing marks in the description, and do not imply an absolute order in time or space, nor do they imply that the terms with this serial number are the same as those with the The same terms of...

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Abstract

The invention relates to a spiking neural network training method, a storage medium, a chip and an electronic product. In order to overcome the problem that an algorithm and hardware are difficult to fit due to device mismatch in the prior art and enable a trained network to well adapt to hardware characteristics of different sensors, various event-based and rate-based enhancements are carried out on training data; comprising the steps of random thermal noise generation, shot noise simulation, adaptive data rate adjustment and random firmware necrosis, training is carried out based on enhanced data, and configuration parameters enabling the prediction performance of the pulse neural network to be optimal are obtained. According to the method, the application adaptation problem when different sensors or different environments are connected with pulse neural network hardware is efficiently and uniformly solved, the chip performance is more stable and effective, and the reasoning result is more consistent. The method is suitable for the field of brain-like chips, in particular to the field of pulse neural network training.

Description

technical field [0001] The invention relates to a pulse neural network training method, a storage medium, a chip and an electronic product, in particular to a pulse neural network training method for adapting device mismatch between different sensors, a storage medium, a chip and an electronic product. Background technique [0002] Sensors, such as event cameras (also known as event-driven cameras, dynamic vision sensors), audio front ends, etc., convert changing information into events. Event camera is a novel image sensing hardware device, and it is also one of the research hotspots in recent years. Each pixel of an event camera receives a light change signal independently, and asynchronously sends out a pulse event when the light changes, so it does not have the concept of a frame in a traditional camera. Although there are also cases of processing data generated by event cameras through traditional artificial neural networks (ANN) (usually requiring frame compression pr...

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G06T5/00
CPCG06N3/049G06N3/08G06T5/002
Inventor 李波邢雁南乔宁胡雅伦柯政刘雨杭柯炜杰
Owner SHENZHEN SYNSENSE TECH CO LTD
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