Local learning method and device and medium

A learning method and local technology, applied in machine learning, instruments, biological neural network models, etc., to achieve the effect of reducing power consumption

Pending Publication Date: 2022-05-24
SHANDONG INSPUR SCI RES INST CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Static objects In the case of learning from new data, the model needs to be retrained from scratch and re-uploaded to the MCU, which makes the deployment of TinyML in an industrial environment a challenging task

Method used

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  • Local learning method and device and medium
  • Local learning method and device and medium
  • Local learning method and device and medium

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Embodiment Construction

[0021] In order to make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below with reference to the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0022] The technical solutions provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0023] At present, it is impossible to update the model weight parameters when deploying machine learning models based on TinyML hardware on the market. At the same...

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Abstract

The invention discloses a local learning method and device and a medium. The method comprises the following steps: receiving an input data stream through a TinyML model, reasoning the data stream through the TinyML model to obtain a reasoning data stream, and sending the reasoning data stream to a local learning module; inputting the reasoning data stream into an additional neural network layer of a local learning module, and performing reasoning through the additional neural network layer to obtain a prediction result; a label flow is obtained according to the data flow, an update matrix is determined according to the prediction result and the label flow, and the update matrix is sent to an update layer of a local learning module; and determining an update weight parameter according to the update matrix, and sending the update weight parameter to the additional neural network layer so as to update the additional neural network layer according to the update weight parameter. According to the invention, resource-limited MCU online learning is realized through the local learning system, the model weight is updated locally according to the collected data, and the constraint and limitation of the MCU are effectively solved.

Description

technical field [0001] The present application relates to the technical field of microcontrollers, and in particular, to a local learning method, device and medium. Background technique [0002] Microcontroller Unit (MCU), also known as Single Chip Microcomputer (Single Chip Microcomputer) or single chip microcomputer, is to appropriately reduce the frequency and specifications of the central processing unit (Central Process Unit, CPU), and reduce the frequency and specifications of the memory (memory), Counter (Timer), USB, A / D conversion, UART, PLC, DMA and other peripheral interfaces, and even the LCD driver circuit are integrated on a single chip to form a chip-level computer, which can be controlled in different combinations for different applications. [0003] Tiny Machine Learning (TinyML) is a subfield of machine learning that includes algorithms, hardware, and software that can analyze sensor data and run on extremely low-power devices. [0004] At present, through...

Claims

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Application Information

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IPC IPC(8): G06N3/02G06N3/063G06N20/00
CPCG06N3/02G06N3/063G06N20/00
Inventor 朱翔宇李锐张晖
Owner SHANDONG INSPUR SCI RES INST CO LTD
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