WO2018120019A1 - Appareil et système de compression/décompression destinés à être utilisés avec des données de réseau neuronal - Google Patents

Appareil et système de compression/décompression destinés à être utilisés avec des données de réseau neuronal Download PDF

Info

Publication number
WO2018120019A1
WO2018120019A1 PCT/CN2016/113497 CN2016113497W WO2018120019A1 WO 2018120019 A1 WO2018120019 A1 WO 2018120019A1 CN 2016113497 W CN2016113497 W CN 2016113497W WO 2018120019 A1 WO2018120019 A1 WO 2018120019A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
module
video
encoding
neural network
Prior art date
Application number
PCT/CN2016/113497
Other languages
English (en)
Chinese (zh)
Inventor
陈天石
罗宇哲
郭崎
刘少礼
陈云霁
Original Assignee
上海寒武纪信息科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 上海寒武纪信息科技有限公司 filed Critical 上海寒武纪信息科技有限公司
Priority to PCT/CN2016/113497 priority Critical patent/WO2018120019A1/fr
Publication of WO2018120019A1 publication Critical patent/WO2018120019A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons

Definitions

  • the present invention relates to the field of artificial neural network technologies, and in particular, to an apparatus and system for compression/decompression of neural network data.
  • ANNs Artificial neural networks
  • NNNs neural networks
  • neural networks have made great progress in many fields such as intelligent control and machine learning.
  • neural networks have once again become a hot issue in the field of artificial intelligence.
  • the size of neural networks has become larger and larger.
  • Google Inc. have proposed the concept of “large-scale deep learning” and hope to build intelligent computer systems through Google as a platform to integrate global information.
  • the present invention provides an apparatus and system for compression/decompression of neural network data to reduce the pressure of storage space and memory access bandwidth.
  • a compression apparatus for neural network data includes: a model conversion module 120 for converting neural network data into video-like data; and a data encoding module 131 connected to the model conversion module 120 for video encoding The data is encoded to obtain a compressed result.
  • the video-like data refers to a model conversion module.
  • each of the original neural network data is converted into a series of integer values within a preset range, corresponding to the representation of one pixel, which together constitute the corresponding video data.
  • the model conversion module 120 converts the neural network data into video-like data in one of two ways:
  • model conversion module 120 For neural network data with a data range of [-b, a], the model conversion module 120 operates as follows:
  • I is an integer in the interval [0, 255], that is, a representation of one pixel;
  • w is the true data value of the neural network data in the range [-b, a];
  • the model conversion module 120 converts the weights and offsets of the hidden layer neurons corresponding to each feature map in the convolutional neural network data, and weights and biases
  • the integers obtained after the conversion are integrated to obtain data of the corresponding video frame, and the video data of the video frame is obtained by combining the weights of the hidden layer neurons corresponding to the plurality of feature maps and the data of the similar video frames obtained by the offset.
  • the data encoding module 131 includes: an encoding sub-module for encoding the video data in a video encoding manner to obtain a data encoding result; and an integration sub-module for The data encoding result and the encoding process information are integrated to obtain a compressed result.
  • the encoding sub-module includes: a prediction unit 130a for performing predictive coding using correlation between video-like data neighboring data; and a transforming unit 130b for processing the predicted unit
  • the video-like data is orthogonally transform-encoded to compress the data
  • the quantization unit 130c is configured to perform quantization coding on the video-like data processed by the transform unit, and reduce the coding length of the data without degrading the data quality
  • the encoding unit 130d is configured to perform rate compression encoding on the video-like data processed by the quantization unit by using statistical characteristics of the data to reduce data redundancy.
  • the encoding sub-module includes: a depth auto-encoder unit 130, configured to further encode the video-like data output by the model conversion module, and output the hidden layer as a coding result; wherein the depth
  • the automatic encoder unit 130 trains by using the video-like data as a training input and an ideal output by minimizing the reconstruction error. It is basically the same data as the input class video data.
  • the compression device of the present invention further includes: a data cache module 140 for buffering neural network data; and a controller module 110 coupled to the data cache module 140, the model conversion module 120, and the data encoding module 131 for transmitting Control instructions to perform the following operations:
  • the data cache instruction is sent to the data cache module 140 to obtain the compression result from the data encoding module 131, and the compression result is cached.
  • a decompression apparatus for neural network data includes: a data decoding module 132, configured to obtain a compression result, and decode the compression result by using a video decoding manner corresponding to the compression result; and a model conversion module 120 connected to the data decoding module 132, Used to restore the decoded class video data to neural network data.
  • the data decoding module 132 includes: a de-integration sub-module for de-integrating the compression result to obtain a data encoding result and encoding process information; and a decoding sub-module for The coding mode information is extracted from the coding process information, and the data coding result is decoded by using a decoding mode corresponding to the coding mode information to obtain a class video. data.
  • the model conversion module restores the decoded video-like data to neural network data in one of two ways:
  • the model conversion module 120 operates according to the following formula to obtain the true data values of the neural network data:
  • w is the true data value of the neural network data in the range [-b, a]
  • I is the video-like data, which is an integer in the interval [0, 255].
  • the model conversion module 120 converts the data of the corresponding video frame in the video-like data, and converts each frame into a hidden layer neuron corresponding to a feature map of the convolutional neural network. Weight and offset, the data converted by each frame is integrated to obtain the weight and offset of the hidden layer neurons corresponding to each feature map of the convolutional neural network.
  • the decompression device of the present invention further includes: a data cache module 140 for buffering the compression result; and a controller module 110, coupled to the model conversion module 120, the data decoding module 132, and the data cache module 140, for Release the control instructions to do the following:
  • a data conversion instruction is sent to the model conversion module 120 to convert the video-like data into neural network data.
  • a system for compression/decompression of neural network data includes: a compression device, which is the compression device described above; and a decompression device, which is the decompression device described above; wherein the compression device and the decompression device share a data cache module 140, a controller module 110, and a model Conversion module 120.
  • the apparatus and system for compressing/decompressing neural network data of the present invention have at least one of the following beneficial effects:
  • the present invention can achieve high-efficiency compression and decompression of a large-scale neural network model, thereby greatly reducing the storage space and transmission pressure of the neural network model, thereby adapting to the trend of expanding the size of the neural network in the era of big data.
  • FIG. 1 is a block diagram showing the structure of a compression apparatus for compressing neural network data according to a first embodiment of the present invention.
  • FIG. 2 is a schematic structural view of a data encoding module in the compression device shown in FIG. 1.
  • FIG. 3 is a flow chart of a controller module in FIG. 1 transmitting a control command to perform an operation.
  • FIG. 4 is a schematic structural diagram of a decompression apparatus for decompressing a neural network data compression result according to a second embodiment of the present invention.
  • FIG. 5 is a schematic structural diagram of a data decoding module in the decompression device shown in FIG. 4.
  • FIG. 6 is a controller module of the decompression device shown in FIG. 4 transmitting a control command to perform an operation Flow chart.
  • FIG. 7 is a schematic structural diagram of a compression/decompression system for neural network data compression results according to a third embodiment of the present invention.
  • 110-controller module 120-model conversion module; 140-data cache module;
  • 130a-prediction unit 130b-transform unit; 130c-quantization unit;
  • 130d-entropy coding unit 130e-depth automatic encoder unit
  • Video coding and decoding technology is a very mature technology.
  • Traditional video coding and decoding technology uses techniques such as prediction, transform and entropy coding. After deep learning, the use of deep neural networks for video encoding and decoding has become a new research hotspot.
  • the applicant found that the neural network data has the same local correlation as the pixels of the video image. Therefore, using the video codec method to encode and decode the neural network model, and then compressing the neural network model, it will be a A viable technical route.
  • a compression apparatus for compressing neural network data includes: a controller module 110, a model conversion module 120, a data encoding module 131, and a data cache module 140.
  • the data cache module 140 is configured to cache the neural network data obtained by the external storage module 200.
  • the model conversion module 120 is coupled to the data cache module 140 for converting neural network data into video-like data.
  • the data encoding module 131 is coupled to the model conversion module 120 for encoding video-like data in a video encoding manner.
  • the controller module 110 is connected to the model conversion module 120, the data encoding module 131, and the data cache module 140, and is used to issue control commands to the three to coordinate the work.
  • the controller module 110 sends a control instruction to perform the following operations:
  • Step S302 sending a data read instruction to the data cache module 140, requesting the neural network data to the external storage module 200, and buffering the neural network data;
  • neural network data refers to data that characterizes the type, structure, weight and neuron characteristics of the neural network.
  • Step S304 sending a data read instruction to the model conversion module 120 to read the neural network data from the data cache module 140;
  • Step S306 sending a data conversion instruction to the model conversion module 120, so that it converts the read neural network data into video-like data;
  • the video-like data herein refers to the original neural network data converted into a series of integer values within a preset range after the conversion by the model conversion module, such as an integer value in the interval [0, 255], Corresponding to the representation of one pixel, these integers together constitute video-like data.
  • the following two examples of specific neural network data are described:
  • the model conversion module can operate as follows:
  • I is an integer in the interval [0, 255], that is, a representation of one pixel;
  • w is the true data value of the neural network data in the range of [-b, a].
  • the model conversion module 120 converts the weights and offsets of the hidden layer neurons corresponding to each feature map in the convolutional neural network data, and integrates the integers obtained by the weight conversion to obtain data of the corresponding video frame.
  • the weight of the hidden layer neurons corresponding to the plurality of feature maps and the data of the similar video frames obtained by the offsets are combined to obtain the video-like data.
  • Step S308 sending a data read instruction to the data cache module 140, requesting the class conversion data to be requested by the model conversion module 120, and performing buffering;
  • Step S310 sending a data read instruction to the data encoding module 131 to read the video-like data from the data cache module 140;
  • Step S312 sending a data encoding instruction to the data encoding module 131, where the encoding instruction includes information of the encoding mode, so that the unit-type video data corresponding to the encoding mode is encoded to obtain a data encoding result;
  • the data encoding module 131 includes: an encoding sub-module for encoding the video data in a video encoding manner to obtain a data encoding result; and an integration sub-module for using the data
  • the coding result is integrated with the coding process information to obtain a compression result.
  • the encoding sub-module further includes: a prediction unit 130a, a transform unit 130b, a quantization unit 130c, an entropy encoding unit 130d, and a depth auto-encoder unit 130e.
  • the prediction unit 130a performs predictive coding using correlation between adjacent video-like data (neural network data).
  • the similarity of the weights of the neural network units corresponding to different feature maps predicts the weight of the neural network, and encodes the difference between the predicted value and the actual value to achieve the purpose of compression.
  • the transform unit 130b performs orthogonal transform coding on the video-like data processed by the prediction unit 130a, thereby achieving the purpose of compression.
  • the quantization unit 130c quantizes the video-like data processed by the transform unit, which can reduce the coding length of the data without degrading the data quality.
  • Q step is the quantization step size
  • FQ(u , v) is the quantized value of F(u,v)
  • round() is the rounding function (the output is the integer closest to the input real number).
  • the entropy coding unit 130d performs rate compression coding on the video-like data processed by the quantization unit by using the statistical characteristics of the data, such as Huffman coding and arithmetic coding.
  • the entropy encoding unit 130d can decode the encoded data by adopting a decoding method corresponding to the encoding method.
  • the coding mode includes: prediction, transform, quantization, and entropy coding.
  • the video-like data sequentially passes through the prediction unit 130a, the transform unit 130b, the quantization unit 130c, and the entropy coding unit 130d.
  • the output of one module is the input of the latter module.
  • a set of video data after being subjected to the prediction unit 130a, becomes a coding result of the difference between the predicted value and the actual value, enters the transform unit 130b, is further compressed by the two-dimensional DCT transform, and then enters the quantization unit 130c so that the code length thereof is shortened.
  • the coding redundancy is reduced by the Huffman coding of the entropy coding unit 130d, thereby achieving a better compression effect.
  • the depth auto-encoder unit 130e encodes the data by using the working principle of the depth auto-encoder.
  • the depth auto-encoder unit 130 trains the video-like data as a training input and an ideal output by minimizing the reconstruction error, so that the output becomes substantially the same data as the input-type video data, so that the depth auto-encoder unit will
  • the hidden layer output is used as the encoding result, and the final output is used as the decoding result. Since the number of neurons in the hidden layer is less than the number of input neurons, the input data can be compressed.
  • the depth autoencoder unit encodes the information of the decoder side of the deep autoencoder and encodes the encoded result for decoding.
  • the coding command may be one of the foregoing coding modes, or may be a combination of the above two coding modes, or may be other video coding modes.
  • the sequence of instructions in the controller module can be determined by a program written by the user, and the neural network data can be compressed by using a compression method that the user desires to use. Users can combine different coding methods by writing related programs.
  • the controller module compiles the relevant program into Order, and decode the instructions into relevant control instructions to achieve control of each module and encoding process.
  • the process of compressing data is essentially a process of encoding data, and the encoding process in the above process can be equivalently regarded as part of a compression process or a compression process.
  • Step S314 sending an integration instruction to the data encoding module 131, so that the data encoding result and the encoding process information are integrated to obtain a compression result;
  • the compression result includes two parts: the first part is the data encoding result of the neural network data, and the second part is the encoding process information.
  • the coding process information may include: information of the coding mode and information of the decoding end of the depth auto-encoder (when the depth auto-encoder unit is used).
  • Step S316 sending a data cache instruction to the data cache module 140, obtaining a compression result from the data encoding module 131, and buffering the compression result;
  • Step S318, sending a data storage instruction to the data cache module 140 to save the compression result to the external storage module 200.
  • the compression result is output to the external storage module, but in other embodiments of the present invention, the compression result may be directly transmitted, or the compression result may be cached in the data encoding module.
  • the 131 or the data cache module 140 is an optional implementation of the present invention.
  • a decompression apparatus for decompressing neural network data compression results is provided.
  • the decompression device for decompressing the neural network data compression result is similar to the compression device of the first embodiment, and includes: a controller module 110, a model conversion module 120, a data decoding module 132, and Data cache module 140.
  • the connection relationship of each module in the decompression device in this embodiment is similar to the connection relationship of the compression device in the first embodiment, and will not be described in detail herein.
  • the data cache module 140 is configured to cache the compression result.
  • the data decoding module 132 is connected to the model conversion module 120 for using a video decoder corresponding to the compression result.
  • the compression result is decoded.
  • the model conversion module 120 is connected to the data decoding module (132) for restoring the decoded video-like data to neural network data.
  • the controller module 110 is connected to the model conversion module 120, the data decoding module 132, and the data cache module 140, and is configured to issue control instructions to the three to coordinate the work.
  • the operations performed by the respective modules in the decompression device of the present embodiment are inverse to the operations performed by the corresponding modules of the compression device of the first embodiment.
  • the controller module 110 sends a control instruction to perform the following operations:
  • Step S602 sending a data read instruction to the data cache module 140, requesting the compression result to the external storage module 200, and buffering the compression result;
  • the compression result here includes two parts: the first part is the data encoding result of the neural network data, and the second part is the encoding process information.
  • Step S604 sending a data read instruction to the data decoding module 132 to read the compression result from the data cache module 140;
  • Step S606 sending a de-integration instruction to the data decoding module 132, so that it decodes the encoding process information and the data compression result from the compression result;
  • Step S608 sending a data read instruction to the data decoding module 132, and reading the encoding process information from the data decoding module 132;
  • Step S610 selecting a decoding instruction according to the encoding process information
  • the encoding process information may include: information of the encoding mode and information of the decoding end of the depth autoencoder (when the depth autoencoder unit is used). Therefore, it is possible to obtain from the encoding process information which encoding mode or combination of encoding modes is used to encode the neural network data, and accordingly generate corresponding decoding instructions.
  • the decoding instruction includes which decoding method is used to decode the data encoding result in the compression result.
  • Step S612 sending a decoding instruction to the data encoding and decoding module 132, so that it decompresses the data compression result in the compression result to obtain video-like data;
  • the data decoding module 132 includes: a de-integration sub-module, configured to de-integrate the compression result to obtain a data encoding result and encoding process information; and a decoding sub-module, configured to extract the encoding mode information from the encoding process information,
  • the data encoding result is decoded by using a decoding method corresponding to the encoding mode information to obtain video-like data.
  • the decoding submodule further includes: a prediction unit 130a, a transformation unit 130b, a quantization unit 130c, and an entropy coding unit 130d.
  • depth autoencoder unit 130e The operations performed by each unit are inverse to the related operations in the encoding module.
  • Entropy encoding unit 130d may perform an entropy decoding process corresponding to the entropy encoding method used when encoding the data, such as a decoding process of Huffman encoding.
  • the quantization unit 130c performs inverse quantization processing on the compression result processed by the entropy coding unit.
  • the following inverse quantization process is used:
  • the transform unit 130b performs inverse orthogonal transform on the data compression result processed by the quantization unit to perform decoding.
  • Equation 2-1 the inverse two-dimensional discrete cosine transform for an N ⁇ N matrix is expressed as:
  • the prediction unit 130a decodes the compression result processed by the transformation unit using the correlation between adjacent data in the original neural network data.
  • the prediction unit 130a may add the predicted value to the correlation difference to restore the original value.
  • the depth auto-encoder unit 130e decodes the neural network data encoded by the deep auto-encoder (as indicated by a broken line in FIG. 5).
  • the depth auto-encoder unit 130e first decodes the decoding end information of the depth auto-encoder used in the encoding from the input data, constructs a decoder using the decoding-end information, and uses the decoder pair.
  • the neural network data encoded by the deep autoencoder is decoded.
  • the encoding instruction may be an encoding method or a combination of two or more encoding methods.
  • the data decoding module 132 sequentially decodes the data by using a corresponding decoding manner.
  • the encoded data sequentially passes through the entropy encoding module 130d, and the quantization module 130c.
  • the transform module 130b and the prediction module 130a the output of the previous module is the input of the latter module.
  • the compressed neural network data of a set of input data encoding and decoding modules is decoded by the entropy encoding module 130d for the decoding process corresponding to the Huffman encoding, and the decoding result is input to the quantization unit 130c for inverse quantization, and then enters the transform unit 130b for reverse.
  • the transform finally enters the prediction unit 130a so that the predicted value is added to the correlation difference, thereby outputting the decoded result.
  • Step S614 sending a data read instruction to the data cache module 140, and causing the data cache module 140 to read the video-like data from the data decoding module 132, and buffering;
  • Step S616 sending a data read instruction to the model conversion module 120, so that the model conversion module 120 reads the video-like data from the data cache module 140;
  • Step S618, sending a data conversion instruction to the model conversion module 120, so that the model conversion module 120 converts the video-like data into neural network data;
  • the model conversion module (120) operates according to the following formula to obtain real data values of the neural network data:
  • w is the true data value of the neural network data in the range [-b, a]
  • I is the video-like data, which is an integer in the interval [0, 255].
  • the model conversion module 120 converts the data of the corresponding video frame in the video-like data, and converts each frame into a hidden layer corresponding to a feature map of the convolutional neural network.
  • the weights and offsets of the neurons integrate the data converted by each frame to obtain the weights and offsets of the hidden layer neurons corresponding to the feature maps of the convolutional neural network.
  • Step S620 sending a data read instruction to the data cache module 140, and causing the data cache module 140 to request the neural network data from the model conversion module 120, and buffering;
  • Step S622 send a data write command to the data cache module 140, so that the data cache module 140 writes the neural network data to the external storage module 200;
  • the decoding result is output to the external storage module, but in other embodiments of the present invention, the decoding result may be directly transmitted, or Cache the decoding result in the model conversion module or the data cache module, which are optional implementations of the present invention.
  • the decompression process is essentially a decoding process, so the decoding process in the above process can be equivalently regarded as part of the decompression process or the decompression process.
  • a compression/decompression system is also provided.
  • the compression/decompression system of the present embodiment integrates the compression device of the first embodiment and the decompression device of the second embodiment. And, the compression device and the decompression device share the controller module 110, the model conversion module 120, and the data cache module 140. And, the data encoding module 131 in the compression device and the data decoding module 132 in the decompression device are integrated into the data encoding/decoding module 130.
  • the data encoding module 131 and the data decoding module share a prediction unit 130a, a transform unit 130b, a quantization unit 130c, an entropy encoding unit 130d, and a depth auto encoder unit 130e.
  • the neural network data is stored in the external storage module 200; then, the controller module 110 sends a control command to the relevant module to control the compression process; the data cache module 140 reads the neural network data from the external storage module and caches; and then, the model
  • the conversion module 120 reads the neural network data from the data cache module 140 and converts it into class video data, and then stores the video data to the data cache module 140; then, the data encoding module 131 reads the class from the data cache module 140.
  • the video data which in turn passes through the processing of the prediction unit 130a, the transform unit 130b, the quantization unit 130c, and the entropy encoding unit 130d completes the compression process; subsequently, the data buffer module 140 reads the compressed data from the data encoding and decoding module 30. Finally, the data cache module 140 writes the compression result to the external storage module.
  • the data to be decompressed is stored in the external storage module 200, and the data is compressed by the prediction, transformation, quantization, and entropy coding processes of the neural network data; in the following process, the controller module 110 sends the control instruction.
  • the cache module 140 reads the data to be decompressed from the external storage module 200.
  • the data decoding module 132 reads the data to be decompressed from the data buffer module 140, and the data is subjected to processing by the entropy encoding unit 130d, the quantization unit 130c, the transform unit 130b, and the prediction unit 130a, and decompressed into video-like data.
  • the data cache module 140 then reads the video-like data from the data encoding and decoding module 30. Subsequently, the data cache module 140 stores the video-like data to the model conversion module 120, which converts it into neural network data. Finally, the data cache module 140 reads the neural network data from the model conversion module 120 and writes it to the external storage module 200.
  • the invention can be applied to the following (including but not limited to) scenarios: data processing, robots, computers, printers, scanners, telephones, tablets, smart terminals, mobile phones, driving recorders, navigators, sensors, cameras, cloud servers , cameras, camcorders, projectors, watches, earphones, mobile storage, wearable devices and other electronic products; aircraft, ships, vehicles and other types of transportation; televisions, air conditioners, microwave ovens, refrigerators, rice cookers, humidifiers, washing machines, Electric lights, gas stoves, range hoods and other household appliances; and including nuclear magnetic resonance instruments, B-ultrasound, electrocardiograph and other medical equipment.
  • the external storage module and the data cache module may also exist in a whole form, that is, the two modules are merged into one module having a storage function;
  • the external storage module and the data cache module may also exist in a form of local storage distributed among the modules.
  • the external storage module can be replaced by a hard disk
  • the external storage module can be replaced by an input/output module for inputting and outputting data.
  • the present invention can realize high-efficiency compression and decompression of a large-scale neural network model, thereby greatly reducing the storage space and transmission pressure of the neural network model, thereby adapting to the trend of expanding the size of the neural network in the era of big data, and can be applied to the nerve.
  • Various fields of network data have strong promotion and application value.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Theoretical Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Artificial Intelligence (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Neurology (AREA)
  • Compression Or Coding Systems Of Tv Signals (AREA)

Abstract

L'invention concerne un appareil et un système de compression/décompression destinés à être utilisés avec des données de réseau neuronal. Ledit appareil de compression comprend : un module de conversion de modèle (120), qui est utilisé pour convertir des données de réseau neuronal en données de type vidéo, et un module de codage de données (131), qui est connecté au module de conversion de modèle (120) et qui est utilisé pour coder les données de type vidéo au moyen d'un procédé de codage vidéo pour obtenir un résultat de compression. Ledit appareil de décompression comprend : un module de décodage de données (132), qui est utilisé pour obtenir un résultat de compression et qui décode le résultat de compression en utilisant un procédé de décodage vidéo correspondant au résultat de compression ; et un module de conversion de modèle (120), qui est connecté au module de décodage de données (132) et qui est utilisé pour restaurer des données de type vidéo décodées en données de réseau neuronal. Ledit système comprend ledit appareil de compression et ledit appareil de décompression. L'appareil et le système compressent/décompressent des données de réseau neuronal grâce à des procédés de codage et de décodage vidéo, atteignant ainsi un taux de compression élevé, ce qui réduit considérablement l'espace de stockage et la charge de transmission de modèles de réseau neuronal.
PCT/CN2016/113497 2016-12-30 2016-12-30 Appareil et système de compression/décompression destinés à être utilisés avec des données de réseau neuronal WO2018120019A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/CN2016/113497 WO2018120019A1 (fr) 2016-12-30 2016-12-30 Appareil et système de compression/décompression destinés à être utilisés avec des données de réseau neuronal

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2016/113497 WO2018120019A1 (fr) 2016-12-30 2016-12-30 Appareil et système de compression/décompression destinés à être utilisés avec des données de réseau neuronal

Publications (1)

Publication Number Publication Date
WO2018120019A1 true WO2018120019A1 (fr) 2018-07-05

Family

ID=62706542

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2016/113497 WO2018120019A1 (fr) 2016-12-30 2016-12-30 Appareil et système de compression/décompression destinés à être utilisés avec des données de réseau neuronal

Country Status (1)

Country Link
WO (1) WO2018120019A1 (fr)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10872186B2 (en) 2017-01-04 2020-12-22 Stmicroelectronics S.R.L. Tool to create a reconfigurable interconnect framework
CN112131429A (zh) * 2020-09-16 2020-12-25 北京影谱科技股份有限公司 一种基于深度预测编码网络的视频分类方法及系统
US11227086B2 (en) 2017-01-04 2022-01-18 Stmicroelectronics S.R.L. Reconfigurable interconnect
US11531873B2 (en) 2020-06-23 2022-12-20 Stmicroelectronics S.R.L. Convolution acceleration with embedded vector decompression
US11593609B2 (en) 2020-02-18 2023-02-28 Stmicroelectronics S.R.L. Vector quantization decoding hardware unit for real-time dynamic decompression for parameters of neural networks

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105612744A (zh) * 2013-10-01 2016-05-25 索尼公司 视频数据编码和解码
WO2016118257A1 (fr) * 2015-01-22 2016-07-28 Qualcomm Incorporated Compression et réglage fin de modèles
CN105844330A (zh) * 2016-03-22 2016-08-10 华为技术有限公司 神经网络处理器的数据处理方法及神经网络处理器
CN106169961A (zh) * 2016-09-07 2016-11-30 北京百度网讯科技有限公司 基于人工智能的神经网络的网络参数处理方法及装置
CN106203624A (zh) * 2016-06-23 2016-12-07 上海交通大学 基于深度神经网络的矢量量化系统及方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105612744A (zh) * 2013-10-01 2016-05-25 索尼公司 视频数据编码和解码
WO2016118257A1 (fr) * 2015-01-22 2016-07-28 Qualcomm Incorporated Compression et réglage fin de modèles
CN105844330A (zh) * 2016-03-22 2016-08-10 华为技术有限公司 神经网络处理器的数据处理方法及神经网络处理器
CN106203624A (zh) * 2016-06-23 2016-12-07 上海交通大学 基于深度神经网络的矢量量化系统及方法
CN106169961A (zh) * 2016-09-07 2016-11-30 北京百度网讯科技有限公司 基于人工智能的神经网络的网络参数处理方法及装置

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10872186B2 (en) 2017-01-04 2020-12-22 Stmicroelectronics S.R.L. Tool to create a reconfigurable interconnect framework
US11227086B2 (en) 2017-01-04 2022-01-18 Stmicroelectronics S.R.L. Reconfigurable interconnect
US11562115B2 (en) 2017-01-04 2023-01-24 Stmicroelectronics S.R.L. Configurable accelerator framework including a stream switch having a plurality of unidirectional stream links
US11675943B2 (en) 2017-01-04 2023-06-13 Stmicroelectronics S.R.L. Tool to create a reconfigurable interconnect framework
US11593609B2 (en) 2020-02-18 2023-02-28 Stmicroelectronics S.R.L. Vector quantization decoding hardware unit for real-time dynamic decompression for parameters of neural networks
US11880759B2 (en) 2020-02-18 2024-01-23 Stmicroelectronics S.R.L. Vector quantization decoding hardware unit for real-time dynamic decompression for parameters of neural networks
US11531873B2 (en) 2020-06-23 2022-12-20 Stmicroelectronics S.R.L. Convolution acceleration with embedded vector decompression
US11836608B2 (en) 2020-06-23 2023-12-05 Stmicroelectronics S.R.L. Convolution acceleration with embedded vector decompression
CN112131429A (zh) * 2020-09-16 2020-12-25 北京影谱科技股份有限公司 一种基于深度预测编码网络的视频分类方法及系统

Similar Documents

Publication Publication Date Title
WO2018121670A1 (fr) Dispositifs de compression/ décompression, système, puce et dispositif électronique
Hu et al. Learning end-to-end lossy image compression: A benchmark
US20200160565A1 (en) Methods And Apparatuses For Learned Image Compression
CN110798690B (zh) 视频解码方法、环路滤波模型的训练方法、装置和设备
WO2018120019A1 (fr) Appareil et système de compression/décompression destinés à être utilisés avec des données de réseau neuronal
WO2020047756A1 (fr) Procédé et appareil de codage d'image
CN112702592B (zh) 端到端双目图像联合压缩方法、装置、设备和介质
US20200090069A1 (en) Machine learning based video compression
CN103581665A (zh) 转码视频数据
KR20200089635A (ko) 다수의 상이한 비트레이트로 영상 압축을 하기 위한 시스템들 및 방법들
KR102471288B1 (ko) 송신, 수신 장치 및 방법
CN101883284A (zh) 基于背景建模和可选差分模式的视频编/解码方法及系统
US11483585B2 (en) Electronic apparatus and controlling method thereof
WO2023279961A1 (fr) Procédé et appareil de codage d'image vidéo, et procédé et appareil de décodage d'image vidéo
WO2021112852A1 (fr) Reconstruction d'image à base de filigrane
JP2018201117A (ja) 映像符号化装置、映像符号化方法およびプログラム
TW202324308A (zh) 圖像編解碼方法和裝置
WO2022100140A1 (fr) Procédé et appareil de codage par compression, et procédé et appareil de décompression
CN107205151B (zh) 基于混合失真度量准则的编解码装置及方法
Li et al. 3D tensor auto-encoder with application to video compression
CN114189684A (zh) 一种基于jnd算法的视频编码方法、装置、介质及计算设备
US8861880B2 (en) Image processing device and image processing method
CN105306941A (zh) 一种视频编码方法
WO2022155818A1 (fr) Procédé et dispositif de codage d'image, procédé et dispositif de décodage d'image, et codec
WO2023040745A1 (fr) Procédé et appareil de codage de carte de caractéristiques et procédé et appareil de décodage de carte de caractéristiques

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 16925352

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 16925352

Country of ref document: EP

Kind code of ref document: A1