An artificial intelligence processing method and processor supporting online learning

Abstract

The invention provides an artificial intelligence processing method and processor supporting online learning, which adopts a double judgment mode of adaptive template matching, adaptive high / low threshold judgment combined with neural network judgment, so that the system has online learning ability and can adapt to the use of equipment , The user's time series signal characteristics, solve the ubiquitous problem of individual differences in time series signal characteristics, and effectively improve the accuracy of automatic classification. Compared with the traditional neural network backpropagation algorithm to realize the hardware implementation of online learning, the present invention has a small amount of parameters and computation, and only stores a small amount of data. Compared with the online learning of neural network, the power consumption and required storage volume is greatly reduced. All parameters in the online learning hardware module of the present invention can be freely configured according to different application requirements, so that the online learning algorithm can be flexibly deployed on the processor, and is suitable for application scenarios of automatic classification of various time series signals.

Description

technical field [0001] The invention relates to an online learning technology of time series signals, in particular to an artificial intelligence processing technology supporting online learning. Background technique [0002] In real-world scenarios, sensors often collect a variety of time-series signals, such as pickups collecting vocal signals, and wearable electrodes collecting muscle electrical signals, brain electrical signals, and electrocardiographic signals and other physiological electrical signals. This type of time series signal, according to the morphological characteristics of its waveform, can represent the different states of the signal source or reflect the different properties of the signal source, so as to extract useful information for signal classification processing. For example, in the human voice signal, the pronunciation waveforms of the same word are similar, but the pronunciation waveforms of different words are quite different. Therefore, it can be...

AI Technical Summary

Problems solved by technology

However, the manually extracted time-frequency domain features are often unable to summarize the various categories of signals well, the generalization ability is weak, and they do not have online learning ability and cannot adapt to the signal source differences of time series signals, so the accuracy rate will be relatively low.

The signal classification technology based on neural network uses a large amount of training data and labels to automatically learn the waveform characteristics of the signal. More parameters, higher power consumption and hardware resource consumption

And because the training data generally does not contain the data of the user or the equipment used, or these data are unknown, and because there are generally individual differences in timing signals, that is, different devices of the same nature / state, timing signals of users, and even There may be large differences in the signals of the same device and users at different times, which leads to the possibility that the neural network parameters may overfit the data in the training set, and extract unnecessary classification features, so that they can be directly applied in the use Automatically classify the signals of equipment and users, and the classification accuracy will drop. This is because the training set does not contain information about such signals of equipment and users, and the generalization ability of the model for time series signals of equipment and users is not strong.

In order to improve the generalization ability of the model, the algorithm needs to conduct online learning based on the data of the equipment and users, and adjust the relevant parameters of the algorithm to adapt to the individual differences of the users. The traditional online learning algorithm of neural network, such as the back propagation algorithm, involves A large number of vector matrix multiplication and addition operations will result in high processing power consumption and hardware resource consumption

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