EP4115337A1 - Procédé et appareil de compression d'un réseau neuronal - Google Patents

Procédé et appareil de compression d'un réseau neuronal

Info

Publication number
EP4115337A1
EP4115337A1 EP21707207.3A EP21707207A EP4115337A1 EP 4115337 A1 EP4115337 A1 EP 4115337A1 EP 21707207 A EP21707207 A EP 21707207A EP 4115337 A1 EP4115337 A1 EP 4115337A1
Authority
EP
European Patent Office
Prior art keywords
neural network
compression
iii
subset
trained neural
Prior art date
Legal status (The legal status 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 status listed.)
Pending
Application number
EP21707207.3A
Other languages
German (de)
English (en)
Inventor
Fabian HÜGER
Serin VARGHESE
Peter Schlicht
Yuan Ma
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Volkswagen AG
Original Assignee
Volkswagen AG
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 Volkswagen AG filed Critical Volkswagen AG
Publication of EP4115337A1 publication Critical patent/EP4115337A1/fr
Pending legal-status Critical Current

Links

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
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
    • 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
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
    • G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles

Definitions

  • the invention relates to a method and a device for compressing a neural network.
  • the invention also relates to a computer program and a data carrier signal.
  • Modern driver assistance systems and driving functions for automated driving are increasingly using machine learning, among other things, to recognize the vehicle environment including other road users (e.g. pedestrians and other vehicles) and to describe their behavior.
  • the evaluation of input data (input) from various sources takes place through deep neural networks, which, among other things, carry out pixel-by-pixel classifications (semantic segmentation) or create enclosing frames (bounding boxes) of recognized objects.
  • CNN convolutional neural networks
  • the convolution networks used here increasingly use a large number of filters and layers, so that the time and computational effort required to process (inference) input data into outputs (output) increases. Since the use of neural networks in the area of automatic driving is subject to severe restrictions with regard to the required computing time due to the dynamic environment and at the same time the hardware (computing capacities) that can be used in vehicles cannot be scaled as desired, the size of the neural network is a limiting factor with regard to the possible uses in such systems.
  • the choice of the elements to be removed is important here. Different elements can influence the output of the network to different degrees. It is therefore important to use selected strategies to select those elements whose removal causes the least effects on the output (quality) and at the same time to check the largest possible number of elements in order to significantly reduce the size of the neural network and thus minimize inference. and to achieve training times.
  • the method comprises the extraction of convolution layers from a trained CNN, each convolution layer containing a kernel matrix with at least one filter formed in a corresponding output channel of the kernel matrix, a feature map set with a feature map that corresponds to each filter.
  • An absolute kernel weight is determined for each kernel and added up over each filter in order to determine a strength of each filter.
  • the strength of each filter is compared to a threshold and a filter is removed if the determined strength is below the threshold.
  • a feature map corresponding to each of the removed filters is removed to prune the CNN.
  • the CNN is retrained to produce a checked CNN with fewer convolutional layers.
  • a method, a computer-readable medium and a system for checking a neural network are known from US 2018/0114114 A1.
  • the method comprises the steps of receiving first order gradients of a cost function relative to slice parameters for a trained neural network and calculating a pruning criterion for each slice parameter based on the first order gradient corresponding to the slice parameter, the pruning criterion indicating an importance of each neuron, which is contained in the trained neural network and is assigned to the slice parameter.
  • the method comprises the additional steps of identifying at least one neuron with the least importance and removing the at least one neuron from the trained neural network in order to generate a tested neural network.
  • Quantization teacher-student learning or AutoML for Model Compression (AMC) are known as further compression methods.
  • AMC AutoML for Model Compression
  • the number of bits is reduced which are necessary to represent parameters of a neural network. So no parameters or entire filters are removed, but representations of the parameters that can be processed more quickly are selected, thus achieving more efficient processing on a given hardware.
  • teacher-student learning two models are trained instead of a single neural network. Here acts the first as a "teacher” and the second as a "student”.
  • the student model is also trained using the input data from the teacher model.
  • the teacher model passes on information to the student model in every step so that the student model can adapt to the teacher model. As a rule, a smaller neural network architecture is used for the resulting student model.
  • AMC AutoML for Model Compression
  • the invention is based on the object of improving a method and a device for compressing a neural network.
  • a method for compressing a neural network is provided, a trained neural network being obtained, with at least one structural information item of the trained neural network being extracted or obtained, the trained neural network subdividing into subsets for compression depending on the at least one structural information item is, wherein a compression method is selected and used for each subset depending on at least one property of the respective subset, and wherein the compressed neural network is provided.
  • a device for compressing a neural network comprising a computing device, the computing device being set up to receive a trained neural network, to extract or receive at least one structural information item of the trained neural network, the trained neural network for compression in To subdivide the at least one structural information into subsets as a function of the at least one structural information item, and to select and use a compression method for each subset as a function of at least one property of the respective subset, and to provide the compressed neural network.
  • the method and the device make it possible to compress a neural network in an improved manner, since different compression methods can be used for individual subsets of the neural network.
  • a compression method is not used for the entire neural network, but a compression method that is most suitable for this can be used for each subset of the neural network, that is, for different sub-units or sub-blocks etc.
  • a trained neural network the subsets of which are locally optimally compressed in each case, is overall improved compared to a compression of the entire neural network using only a single compression method.
  • This is achieved by dividing a fully trained neural network into subsets for compression depending on at least one piece of structural information.
  • the at least one piece of structural information is either extracted from a structure of the trained neural network or the at least one piece of structural information is provided and received.
  • the at least one structure function includes, in particular, a description of the neural network with regard to a structure, that is to say the individual components, such as functional blocks, layers, etc., and their respective interconnection.
  • the at least one structural information item therefore describes, in particular, an internal structural structure of the neural network.
  • a compression method to be used in each case is selected and used as a function of at least one property of the respective subset.
  • the compression then takes place in particular iteratively for each of the specific subsets.
  • a subset of the neural network is compressed by means of the corresponding selected compression method, while the other subsets of the neural network remain unchanged or are not compressed during this time. If this subset of the neural network is compressed, the next subset is compressed, while the other subsets remain unchanged. When all subsets have been compressed with the selected compression method, compression is finished. The neural network compressed in this way is then made available.
  • the provision includes in particular outputting the compressed neural network, for example in the form of a digital data record describing the structure and the weightings or parameters of the compressed neural network.
  • Providing can also include transmitting the compressed neural network to a control device, for example a vehicle, and / or storing the compressed neural network in a memory of the control device so that the compressed neural network can be used there.
  • a control device for example a vehicle
  • a control device for example a vehicle
  • the targeted selection of compression methods for each of the subsets can in particular ensure that any redundancy present in the neural network is minimized.
  • the possibility of individually selecting each of the compression methods for the subsets means that different priorities or objectives can be set for each of the subsets when compressing (e.g. reduced runtime, improved stability, etc.).
  • a neural network is in particular an artificial neural network, in particular a convolutional neural network.
  • the neural network is trained in particular for a specific function, for example the perception of pedestrians in captured camera images.
  • the neural network includes in particular at least one piece of structural information that describes a structure of the neural network, and parameters that describe, for example, weightings of activation functions of individual neurons or of filters, etc.
  • the parameters were determined in particular with the aid of a training data set.
  • Pruning is intended to mean in particular that the structure of the particular subset of the neural network under consideration is changed, in particular trimmed or reduced in size. This is done by removing elements and / or parts (eg parameters or input channels etc.) of the elements from the subset of the neural network. Due to the changed, in particular trimmed, structure, the tested subset of the neural network can be applied to input data with a lower computing power and with a reduced memory requirement. The structure of the tested subset of the neural network is then compressed. The elements of the subset of the neural network to be checked can be selected in various ways.
  • those elements of the subset of the neural network are selected for testing which have the least influence on an output result of the subset of the neural network and / or the neural network. Provision can furthermore also be made for elements to be selected whose outputs are always activations below a predetermined threshold value.
  • the selected elements are collected and compiled, for example, in the form of a list or table or database.
  • a list or table or database for example, an unambiguous identification of a respectively selected element is noted as well as possibly further properties or values, such as for example a maximum, minimum and / or average activation of the element under consideration.
  • the list or the table or database includes a selection criterion used in each case or a value of the respective element associated with the selection criterion used in each case.
  • the individual elements in the list or table or database can be sorted according to a ranking. The pruning is then carried out depending on the order of precedence. For example, a predetermined number of top ranks is checked.
  • a ranking is generated depending on similarities and contexts.
  • the ranking favors filters for which none similar filters exist versus filters for which similar filters exist.
  • the similarity can be determined, for example, by means of Jensen-Shannon divergence, linear dependency, cosine similarity or methods of anomaly detection.
  • the compressed neural network is retrained after compression.
  • training data are again fed to the compressed neural network and the compressed neural network is trained in a manner known per se.
  • Parts of the device in particular the computing device, can be designed individually or collectively as a combination of hardware and software, for example as program code that is executed on a microcontroller or microprocessor.
  • the method can be carried out as a computer-implemented method.
  • the method can be carried out by means of a data processing device.
  • the data processing device comprises in particular at least one computing device and at least one storage device.
  • a computer program is also created, comprising instructions which, when the computer program is executed by a computer, cause the computer to carry out the method steps of the disclosed method in accordance with any of the described embodiments.
  • a data carrier signal is also created that transmits the aforementioned computer program.
  • the subdivision and / or selection of the compression method takes place at least partially as a function of a functionality provided by the respective subset of the trained neural network.
  • a neural network has several functional components, such as feature extractors, fusion blocks, depth estimators, instance segmentations, optical streams, region proposals, classifiers, etc.
  • the most suitable compression method for each of these building blocks can be used.
  • the Fisher pruning technique is particularly good at compressing
  • Subsets that provide functionality of feature extractors are suitable.
  • the Fisher pruning compression method is selected and used in the trained neural network for all such subsets that provide feature extractors.
  • other subsets that provide other functionalities can be compressed using other compression methods.
  • the subdivision and / or selection of the compression method takes place at least partially as a function of a position of the respective subset within the trained neural network.
  • layers of the neural network can be combined into subsets as a function of a position within the neural network and then compressed using a compression method selected in each case.
  • a distinction can be made between starting layers, middle layers and starting layers.
  • a compression method that is best suited for compressing a subset of the neural network under consideration can be determined empirically in particular by comparing different compression methods with one another with regard to a desired result (e.g. a required computing power and / or a memory requirement and / or a functional quality, etc.) will. From the comparison results, it is then possible to derive dependencies and rules about which compression methods are particularly suitable for certain types and / or positions of subsets and which are not.
  • a desired result e.g. a required computing power and / or a memory requirement and / or a functional quality, etc.
  • a heuristic method is used when selecting a respective compression method for the specific subsets.
  • the heuristics can be determined on the basis of the empirical test series described above, for example.
  • a heuristic describes an expected effect of a compression method with regard to a running time and / or an energy requirement and / or a memory requirement and / or with regard to a position and / or a size of the subset to be compressed. Provision can be made for the heuristics to be adapted and / or improved step-by-step by repeated application of the method and the knowledge gained in this way. It is also possible to have the heuristics at least partially created and / or checked and / or adapted by human experts.
  • the heuristics can be used with Deliver a respective result with the help of weighted influencing parameters. This is particularly advantageous if no clear statements about the most suitable compression method are available or possible.
  • the compression methods to be compared with one another are then compared with one another using a respective sum value, for example on the basis of a weighting function, and selected.
  • the respective compression method is carried out as a function of at least one optimization criterion.
  • Optimization criteria are, for example, the following: a functional quality or accuracy of the (compressed) neural network, a required computing power (of the subset considered) and / or a required storage space (the subset considered).
  • the compressions of the subsets of the neural network that are carried out individually are each carried out according to the at least one optimization criterion.
  • an optimization according to the at least one optimization criterion of the entire neural network is considered. If an optimization criterion is, for example, a required running time, the subset is compressed using the associated compression method and a running time of the neural network is determined for each iteration step. The compression method is then carried out or parameterized in such a way that this runtime is optimized, in particular minimized.
  • the at least one optimization criterion is selected and / or specified as a function of at least one property of a control device on which the compressed neural network is to be executed after compression.
  • the properties can include, for example, an available computing power and / or an available memory space. It can be provided here that these properties are queried from the control unit before the compression.
  • the selection and / or the execution of the respective compression method takes place as a function of at least one piece of geographic information and / or at least one piece of context information.
  • the compression method can be adapted to geographical conditions, such as certain features in the environment that are processed by means of the neural network, and / or to a situational context, such as an environmental context (e.g. city, motorway, country road, parking garage, etc.), be adjusted.
  • the compression takes place while taking into account acquired sensor data. This allows properties of the sensor data to be taken into account. In this way, in particular, compression methods can be selected which are more suitable in terms of content and / or structure for a certain type of sensor data.
  • the compression of the neural network is only limited to specific vehicles in the vehicle fleet.
  • a geographically, for example regionally, restricted and / or adapted compression of the neural network can take place here.
  • the compression is carried out, for example, in the vehicle as a function of a geographical position and / or at least one piece of context information. In this way, vehicle-specific compressed neural networks can be provided.
  • the neural network provides a function for the automated driving of a vehicle and / or for a driver assistance of the vehicle and / or for a surrounding area detection and / or surrounding area perception.
  • a vehicle is in particular a motor vehicle.
  • a vehicle can also be another land, air, water, rail or space vehicle.
  • FIG. 1 shows a schematic representation of an embodiment of the device for compressing a neural network
  • the device 1 shows a schematic representation of an embodiment of the device 1 for compressing a neural network 10.
  • the device 1 comprises a computing device 2 and a storage device 3.
  • the computing device 2 can access the storage device 3 and perform arithmetic operations on data stored therein.
  • the computing device 2 carries out the method described in this disclosure.
  • a trained neural network 10 is fed to the computing device 2, the computing device 2 receiving the trained neural network 10.
  • the computing device 2 receives the trained neural network 10 by means of an interface set up for this purpose (not shown).
  • the trained neural network 10 provides a function for automated driving of a vehicle and / or for driver assistance of the vehicle and / or for environment detection and / or environment perception.
  • the computing device 2 extracts or receives at least one piece of structural information from the trained neural network 10.
  • This at least one piece of structural information includes, in particular, information about the structure of the trained neural network 10.
  • the trained neural network 10 is divided into subsets as a function of the at least one structural information item.
  • the computing device 2 selects a compression method depending on at least one property of the respective subset.
  • a property can be a functionality or a position of the subset within the neural network 10, for example.
  • the respective subset of the neural network 10 is compressed by means of the compression method selected in each case.
  • the compression is carried out one after the other for all subsets by means of the respectively selected compression method, the other subsets, that is to say both already compressed and not yet compressed, each remaining unchanged when compressing a subset.
  • the entire neural network is particularly considered in each case.
  • the compressed neural network 11 is provided and, in particular, output in the form of a digital data packet. It can be provided that the compressed neural network 11, in particular as a digital data packet, is transmitted to a control device 51, for example a vehicle, and is loaded into a memory there. The control device 51 can then execute the compressed neural network 11 and apply it to acquired sensor data, for example. Due to the compression, a required running time, a required computing power and / or a memory requirement can be reduced.
  • FIG. 2 shows a schematic flow diagram to illustrate embodiments of the method for compressing a neural network 10. It is provided that the method is carried out in a back-end server 20.
  • the backend server 20 is designed, for example, like the device 1 shown in FIG. 1.
  • a trained neural network 10 and parameters 12 are fed to the backend server 20, the parameters 12 controlling the compression and including at least one optimization criterion 13, for example.
  • the trained neural network 10 provides a function for the automated driving of a vehicle and / or for driver assistance of the vehicle and / or for environment detection and / or environment perception.
  • the compression is controlled by means of a control function 21 provided by the backend server 20.
  • the control function 21 receives the parameters 12 for this purpose.
  • the control function 21 organizes the compression of the trained neural network 10 and has an insight into all intermediate results for this purpose. In particular, the control function 21 organizes the iterative compression.
  • the trained neural network 10 is subdivided into several subsets I, II, III, IV, V by means of a splitter function 22. This takes place as a function of at least one piece of structural information 14, which is extracted from the trained neural network 10 or obtained in some other way, for example received separately from the trained neural network 10.
  • a functionality can be, for example, the following: feature extractors, fusion blocks, depth estimators, instance segmentation, optical streams, Proposals for regions, classifiers, etc.
  • associated subsets which provide these functionalities, are then formed from elements of the neural network 10.
  • the subdivision takes place at least partially as a function of a position of the respective subset I, II, III, IV, V within the trained neural network 10.
  • the position defines, for example, at which point in a processing chain within the trained neural network 10 the respectively considered subset I, II, III, IV, V is located.
  • a distinction can be made, for example, at the beginning, middle and end shifts.
  • a selection function 23 selects a compression method for each of the subsets I, II, III, IV, V as a function of at least one property of the respective subsets I, II, III, IV, V from a catalog 24.
  • a heuristic method is used when selecting a respective compression method for the specific subsets I, II, III, IV, V.
  • the heuristics used here are obtained empirically, for example, that is, by means of targeted test series in which different compression methods are used for different subsets, the effect of which is then evaluated according to specific target criteria.
  • the catalog 24 of compression methods includes, in particular, pruning methods, but can also include other types of compression methods.
  • pruning methods are: Fisher pruning, magnitude pruning and similarity pruning.
  • other compression methods are: quantization, teacher-student learning and AutoML for Model Compression (AMC).
  • the subsets I, II, III, IV, V are each compressed individually by means of central compression modules 25. This is done in particular by always compressing one of the subsets I, II, III, IV, V, while the other subsets I, II, III, IV, V remain unchanged. In order to assess the respective compression result, the entire neural network 10 is considered. If a subset I, II, III, IV, V is compressed, the remaining subsets I, II, III, IV, V are each compressed individually. In the example shown, the following sequence could be used for compression: first subset I, then subset II, then subset III, then subset IV and finally subset V. It can be provided that the compression, in particular the pruning, takes place while taking training data 30 into account. This makes it possible to take into account properties of the training data 30 or an associated data domain.
  • the compressed neural network 11 can be retrained by means of a fine-tuning function 26.
  • the training data 30 are used for this purpose.
  • the compressed neural network 11 is then made available and, in particular, output in the form of a digital data packet. It can then be loaded into a memory of a control device, for example, so that functionality of the compressed neural network 11 can be provided by the control device.
  • the trained neural network 10 can be compressed outside of the backend server 20, in particular in a vehicle 50 of a vehicle fleet, after the subdivision and selection of the respective compression method.
  • the compression methods selected in each case are transmitted to distributed compression modules 27, for example via an air interface (not shown).
  • the compression is then carried out by means of the distributed compression modules 27.
  • sensor data 31 from a current sensor data domain for example from a current environment or context (e.g. a vehicle)
  • a ranking created when checking for activations of elements of the subset I, II, III, IV, V of the trained neural network 10 can be created on the basis of the sensor data 31 which are fed to the neural network 10 as input data.
  • the compression can then take place specifically for an associated sensor data domain.
  • the neural network 11 compressed in this way by means of the distributed compression modules 27 is then retrained and made available by means of the fine-tuning function 26.
  • the respective compression method is carried out as a function of at least one optimization criterion 13.
  • the at least one optimization criterion 13 can include, for example, one or more of the following: a functional quality or accuracy of the (compressed) neural network 11, a required computing power (the considered subset I, II, III, IV, V) and / or a required one Storage space (of the considered subset I, II, III, IV, V).
  • the trained neural network 10 can hereby be optimized in a targeted manner taking into account given boundary conditions.
  • the selection and / or the execution of the respective compression method takes place as a function of at least one piece of geographic information 15 and / or at least one piece of context information 16.
  • the at least one optimization criterion 13 is defined as a function of properties of a control device on which the compressed neural network 11 is then to be executed.
  • properties can be, for example, a provided computing power or an available memory space.

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Abstract

L'invention concerne un procédé de compression d'un réseau neuronal (10), un réseau neuronal entraîné (10) étant obtenu, au moins un élément d'informations de structure provenant du réseau neuronal entraîné (10) étant extrait ou obtenu, le réseau neuronal entraîné (10) étant divisé en sous-ensembles (I, II, III, IV, V) sur la base du ou des éléments d'informations de structure en vue de la compression, un procédé de compression étant sélectionné et utilisé pour chaque sous-ensemble (I, II, III, IV, V) sur la base d'au moins une propriété du sous-ensemble respectif (I, II, III, IV, V), et le réseau neuronal compressé (11) étant prévu. L'invention concerne également un appareil (1) de compression d'un réseau neuronal, un programme d'ordinateur et un signal porteur de données.
EP21707207.3A 2020-03-04 2021-02-18 Procédé et appareil de compression d'un réseau neuronal Pending EP4115337A1 (fr)

Applications Claiming Priority (2)

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DE102020202795.1A DE102020202795A1 (de) 2020-03-04 2020-03-04 Verfahren und Vorrichtung zum Komprimieren eines Neuronalen Netzes
PCT/EP2021/054084 WO2021175615A1 (fr) 2020-03-04 2021-02-18 Procédé et appareil de compression d'un réseau neuronal

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DE102022205679A1 (de) 2022-06-02 2023-12-07 Volkswagen Aktiengesellschaft Verfahren, Computerprogramm und Vorrichtung zum Reduzieren einer erforderlichen Rechenleistung eines Algorithmus der künstlichen Intelligenz, sowie Fortbewegungsmittel

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US11315018B2 (en) 2016-10-21 2022-04-26 Nvidia Corporation Systems and methods for pruning neural networks for resource efficient inference
US20180336468A1 (en) 2017-05-16 2018-11-22 Nec Laboratories America, Inc. Pruning filters for efficient convolutional neural networks for image recognition in surveillance applications

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