CN110337636A - 数据转换方法和装置 - Google Patents
数据转换方法和装置 Download PDFInfo
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- CN110337636A CN110337636A CN201880011394.7A CN201880011394A CN110337636A CN 110337636 A CN110337636 A CN 110337636A CN 201880011394 A CN201880011394 A CN 201880011394A CN 110337636 A CN110337636 A CN 110337636A
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- G06F7/38—Methods or arrangements for performing computations using exclusively denominational number representation, e.g. using binary, ternary, decimal representation
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- G06F7/483—Computations with numbers represented by a non-linear combination of denominational numbers, e.g. rational numbers, logarithmic number system or floating-point numbers
- G06F7/485—Adding; Subtracting
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F7/4876—Multiplying
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- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/30—Arrangements for executing machine instructions, e.g. instruction decode
- G06F9/30003—Arrangements for executing specific machine instructions
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- H—ELECTRICITY
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- H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
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Abstract
一种数据转换方法和装置,该方法包括:根据神经网络的第一目标层的权重对数域位宽和最大权重系数的大小,确定权重基准值;根据该权重基准值和该权重对数域位宽,将该第一目标层中的权重系数转换到对数域。该方法中,权重系数在对数域的权重基准值不是经验值,而是根据权重对数域位宽和最大权重系数确定的,可以改善网络的表达能力,提高网络的准确率。
Description
PCT国内申请,说明书已公开。
Claims (88)
- PCT国内申请,权利要求书已公开。
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
PCT/CN2018/077573 WO2019165602A1 (zh) | 2018-02-28 | 2018-02-28 | 数据转换方法和装置 |
Publications (1)
Publication Number | Publication Date |
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CN110337636A true CN110337636A (zh) | 2019-10-15 |
Family
ID=67804735
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201880011394.7A Pending CN110337636A (zh) | 2018-02-28 | 2018-02-28 | 数据转换方法和装置 |
Country Status (3)
Country | Link |
---|---|
US (1) | US20200389182A1 (zh) |
CN (1) | CN110337636A (zh) |
WO (1) | WO2019165602A1 (zh) |
Families Citing this family (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP3471271A1 (en) * | 2017-10-16 | 2019-04-17 | Acoustical Beauty | Improved convolutions of digital signals using a bit requirement optimization of a target digital signal |
US11037027B2 (en) * | 2018-10-25 | 2021-06-15 | Raytheon Company | Computer architecture for and-or neural networks |
US20220121909A1 (en) * | 2020-03-24 | 2022-04-21 | Lg Electronics Inc. | Training a neural network using stochastic whitening batch normalization |
CN111831356B (zh) * | 2020-07-09 | 2023-04-07 | 北京灵汐科技有限公司 | 权重精度配置方法、装置、设备及存储介质 |
Family Cites Families (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP5960731B2 (ja) * | 2011-03-02 | 2016-08-02 | ドルビー ラボラトリーズ ライセンシング コーポレイション | 局所マルチスケールトーンマッピングオペレータ |
CN103731159A (zh) * | 2014-01-09 | 2014-04-16 | 北京邮电大学 | 一种对先验信息迭代应用的混合域fft多进制和积译码算法 |
US20160026912A1 (en) * | 2014-07-22 | 2016-01-28 | Intel Corporation | Weight-shifting mechanism for convolutional neural networks |
WO2016165120A1 (en) * | 2015-04-17 | 2016-10-20 | Microsoft Technology Licensing, Llc | Deep neural support vector machines |
CN107220025B (zh) * | 2017-04-24 | 2020-04-21 | 华为机器有限公司 | 处理乘加运算的装置和处理乘加运算的方法 |
-
2018
- 2018-02-28 WO PCT/CN2018/077573 patent/WO2019165602A1/zh active Application Filing
- 2018-02-28 CN CN201880011394.7A patent/CN110337636A/zh active Pending
-
2020
- 2020-08-24 US US17/000,915 patent/US20200389182A1/en not_active Abandoned
Also Published As
Publication number | Publication date |
---|---|
US20200389182A1 (en) | 2020-12-10 |
WO2019165602A1 (zh) | 2019-09-06 |
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