| (19) |
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(11) |
EP 3 754 497 A8 |
| (12) |
CORRECTED EUROPEAN PATENT APPLICATION |
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Note: Bibliography reflects the latest situation |
| (15) |
Correction information: |
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Corrected version no 1 (W1 A1) |
| (48) |
Corrigendum issued on: |
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30.06.2021 Bulletin 2021/26 |
| (43) |
Date of publication: |
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23.12.2020 Bulletin 2020/52 |
| (22) |
Date of filing: 19.07.2019 |
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| (51) |
International Patent Classification (IPC):
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| (84) |
Designated Contracting States: |
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AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL
NO PL PT RO RS SE SI SK SM TR |
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Designated Extension States: |
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BA ME |
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Designated Validation States: |
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KH MA MD TN |
| (30) |
Priority: |
18.04.2019 CN 201910315962
23.05.2019 CN 201910436801
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| (62) |
Application number of the earlier application in accordance with Art. 76 EPC: |
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19816184.6 / 3751475 |
| (71) |
Applicant: Cambricon Technologies Corporation Limited |
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Beijing 100190 (CN) |
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| (72) |
Inventors: |
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- ZHANG, Yao
Beijing, 100190 (CN)
- MENG, Xiaofu
Beijing, 100190 (CN)
- LIU, Shaoli
Beijing, 100190 (CN)
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| (74) |
Representative: Huang, Liwei |
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Cäcilienstraße 12
40597 Düsseldorf
40597 Düsseldorf (DE) |
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Remarks: |
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This application was filed on 17-12-2019 as a divisional application to the application
mentioned under INID code 62. |
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| (54) |
DATA PROCESSING METHOD AND RELATED PRODUCTS |
(57) The present disclosure discloses a data processing method and related products, in
which the data processing method includes: generating, by a general-purpose processor,
a binary instruction according to device information of an AI processor, and generating
an AI learning task according to the binary instruction; transmitting, by the general-purpose
processor, the AI learning task to the cloud AI processor for running; receiving,
by the general-purpose processor, a running result corresponding to the AI learning
task; and determining, by the general-purpose processor, an offline running file according
to the running result, where the offline running file is generated according to the
device information of the AI processor and the binary instruction when the running
result satisfies a preset requirement. By implementing the present disclosure, the
debugging between the AI algorithm model and the AI processor can be achieved in advance.