64 results about "Second quantization" patented technology

A method and an apparatus for storing data points are provided. The method comprises: receiving the plurality of data points, each data point of the plurality of data points being represented in a floating-point representation; quantizing each one of the first plurality of data points, by: executing, during a first quantization phase: converting each data point of the plurality of data points into a corresponding first data point of a plurality of first data points; executing, during a second quantization phase: applying, to each first data point of the plurality of first data points a clamping function, thereby converting each first data point of the plurality of first data points into a corresponding second data point of a plurality of second data points; and storing the plurality of second points for further calculations instead of the first plurality of data points.

The invention provides a floating-point number quantization method and device, a floating-point number inverse quantization method and device, equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises: based on a compression rule, determining quantization information corresponding to an index of each floating-point number in floating-point numbers to be quantized, the quantization information corresponding to the index of each floating-point number comprising a first quantization value and / or a second quantization value, the compression rule comprises a rule that an index which indicates that quantization information with a low compression degree belongs to is greater than an index which indicates that quantization information with a high compression degree belongs to, and for each floating-point number in the floating-point numbers to be quantized, based on a first quantization value corresponding to the floating-point number, performing quantization processing on a mantissa of the floating-point number, and / or performing quantization processing on the mantissa of the floating-point number based on a second quantization value corresponding to the floating-point number. And quantizing the index of the floating-point number based on the second quantized value corresponding to the floating-point number. By adopting the method and the device, the required average error can be met during quantization.

A device for decoding a bitstream of encoded mesh data is configured to in response to determining that first transform parameters are overridden, infer a value of a second syntax element to be equal to a first value indicating that second transform parameters are present in the bitstream of encoded mesh data; in response to the value of the second syntax element being equal to the first value, determine the second transform parameters from a second syntax structure; in response to determining that the first quantization parameters are overridden, infer a value of a third syntax element to be equal to a first value indicating that second quantization parameters are present in the bitstream of encoded mesh data; and in response to the value of the third syntax element being equal to the first value, determine second quantization parameters from a third syntax structure.

The invention discloses a traffic flow prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a traffic flow sequence of a target intersection in a first historical time period; standardizing each first traffic flow data of the traffic flow sequence, and determining corresponding second traffic flow data; quantizing each second traffic flow data in a preset historical window, and determining each corresponding first quantized character; inputting each first quantized character into the large language model, and determining a second quantized character of at least one sub-time period in the prediction window based on the large language model; and finally, carrying out inverse quantization and inverse standardization on the second quantization character, and determining corresponding predicted traffic data. And traffic flow prediction based on the large language model is realized. The problems of high popularization and generalization cost and poor expandability of traffic flow prediction based on statistics, machine learning and deep learning prediction models in the prior art are avoided. And the popularization and generalization performance of the traffic flow prediction model is improved.

The invention discloses a language task processing method and electronic equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps: selecting a target weight parameter according to task calibration data and an activation function type of an original task processing model; according to each target weight parameter, the maximum weight parameter and the task calibration data, determining a fitting processing result of the quantization parameter; and determining a first quantization error function and a second quantization error function carrying a scaling factor according to a fitting processing result, and obtaining a numerical value of the scaling factor by minimizing a ratio of the first quantization error function and the second quantization error function. And performing scaling processing on each target weight parameter by using the scaling factor, and quantifying each scaling weight parameter based on the quantization precision parameter to obtain a task processing model for executing the to-be-processed language task. According to the method and the device, the problem that high accuracy of a language task processing result cannot be ensured on the basis of reducing resources used in a language task execution process in related technologies can be solved, and the language task processing precision is effectively improved.

The present invention relates to the field of artificial intelligence technology, and discloses a quantization operation method, an electronic device, and a program product for a neural network model. The quantization operation method includes: determining a first operator and a second operator that need to perform an association operation, where the first operator has first output data and the second operator has second output data; obtaining a common quantization coefficient based on a first quantization coefficient corresponding to the first output data and a second quantization coefficient corresponding to the second output data; obtaining first quantization data corresponding to the first output data according to the first output data and the common quantization coefficient, and obtaining second quantization data corresponding to the second output data according to the second output data and the common quantization coefficient; performing an association operation on the first quantization data and the second quantization data corresponding to the first operator and the second operator to obtain a quantization operation result of the association operation of the first operator and the second operator. Based on the above solution, the model operation speed and accuracy can be effectively improved.

An encoding method, an encoding device, a decoding method, a decoding device, and a transmission system, relating to the technical field of signal processing. The encoding method comprises: performing first quantization processing on a signal to be encoded, to obtain an initial quantized signal (110); performing second quantization processing on the signal to be encoded, to obtain a first multiple-description quantized signal (120), wherein the number of first pulses used in the first quantization processing is greater than the number of second pulses used in the second quantization processing; on the basis of the initial quantized signal and the first multiple-description quantized signal, obtaining a second multiple-description quantized signal of the signal to be encoded (130); and generating a bitstream on the basis of the first multiple-description quantized signal and the second multiple-description quantized signal (140).

An encoder, includes: memory; and circuitry coupled to the memory, in which in a first encoding process, the circuitry quantizes a current block using a first quantization matrix in both of a first case where the circuitry performs primary transform and the circuitry does not perform secondary transform and a second case where the circuitry performs both the primary transform and the secondary transform, in a second encoding process, the circuitry quantizes the current block using a second quantization matrix in a third case where the circuitry performs primary transform and the circuitry does not perform secondary transform, and in a third encoding process, the circuitry quantizes the current block without using a quantization matrix in both of a fourth case where both the primary transform and the secondary transform are skipped and a fifth case where both the primary transform and the secondary transform are performed.

Embodiments of the present application disclose a method and apparatus for determining a quantization parameter offset, a device, a storage medium and a program product. The method comprises: obtaining a first quantization parameter offset and a second quantization parameter offset of a coding unit, the first quantization parameter offset being calculated based on spatial information of the coding unit, and the second quantization parameter offset being calculated based on temporal information of the coding unit; determining representation information of the first quantization parameter offset, the representation information representing whether the first quantization parameter offset is used to improve reconstruction quality of the coding unit; determining a target quantization parameter offset of the coding unit according to the representation information of the first quantization parameter offset, the first quantization parameter offset and the second quantization parameter offset; and encoding the coding unit based on the target quantization parameter offset. The embodiments of the present application can effectively improve coding efficiency.

The invention relates to intra-tree geometric quantization of point clouds. An example apparatus includes one or more processors and a memory storing instructions that, when executed by the processors, cause the processors to receive points representing a point cloud in a three-dimensional space and generate a data structure representing the point cloud. Generating the data structure includes: encoding a location of each point in each dimension as a bit sequence according to a tree data structure; dividing each of the sequences into two or more portions according to a scaling depth; determining that a subset of the points is spatially isolated from the remainder of the points; quantizing each of the portions associated with the subset of the points according to a first quantization step size; quantizing each of the portions associated with the remaining portion of the points according to a second quantization step size; and including the quantized portion in the data structure.

The invention provides a quantization parameter storage method and device, a model reasoning method and device and a storage medium, and relates to the technical field of computers, in particular to the technical field of large models, the technical field of artificial intelligence and the technical field of model quantization. According to the specific implementation scheme, a calculation unit of a processor performs statistics based on reference data to obtain a statistical value of a first quantization parameter of a model; searching a target value of the first quantization parameter and a target value of a second quantization parameter of a model in a search space through the calculation unit based on the statistical value of the first quantization parameter; a target value of the first quantization parameter and a target value of the second quantization parameter are stored in a memory by the calculation unit. The quantization parameters are calculated and stored in advance, so that the quantization parameters which are calculated in advance can be read according to specific use requirements for quantization reasoning, occupation of a memory of a processor is reduced, calculation resources needing to be occupied in the reasoning process are reduced, and the reasoning speed and efficiency are improved.

The present invention provides a data quantization method, device, and storage medium, relating to the field of wireless communication technology. The method includes: performing digital down-conversion processing on an input signal based on a first quantization bit width to obtain a baseband signal; the first quantization bit width is determined based on the peak-to-average ratio of the input signal; performing channel estimation and equalization processing on the baseband signal based on a second quantization bit width; the second quantization bit width is obtained based on real-time calculation of the power of pilot symbols in the baseband signal; demodulating the baseband signal after channel estimation and equalization processing, and dynamically truncating the demodulated soft information based on the second quantization bit width and modulation mode to obtain truncated soft information; performing saturation quantization truncation processing on the truncated soft information, and outputting the truncated soft information to a decoder. This optimizes signal quantization accuracy and resource efficiency.

The invention discloses a model quantification method and device, equipment, a storage medium and a computer program product, and relates to the field of model quantification, and the method comprises the steps: carrying out the training and quantification of a full-precision model disposed on edge equipment, and obtaining a first quantification model which comprises a first layer set; according to a sensitivity index and training cost corresponding to each model layer in the first layer set, a target training layer is selected from the first layer set, and the sensitivity index is used for representing the influence degree of the quantization operation on the model layer; performing quantitative perception training on the target training layer in the first quantitative model to obtain a second quantitative model, the second quantitative model including a second layer set; and performing bit width allocation on each model layer in the second layer set to obtain a target quantitative model. According to the invention, while the precision of the quantitative model is ensured, the cost required by quantitative training is reduced, so that the contradiction between insufficient quantitative precision after training and too high quantitative perception training cost in the prior art is solved.

The application discloses an AVS3 video encoding method, device and equipment and a computer storage medium, relates to the technical field of video processing, and obtains a target video to be encoded; a basic quantization parameter determined according to a set encoding mode is obtained; a first quantization scaling, a first quantization offset, a first quantization boundary and a first quantization step of the set encoding mode are obtained; a second quantization step of the AVS3 video encoding is obtained; conversion information between the second quantization step and the first quantization step is generated; the first quantization scaling, the first quantization offset and the first quantization boundary are respectively converted according to the conversion information, and a second quantization scaling, a second quantization offset and a second quantization boundary are obtained; the basic quantization parameter is adjusted based on the second quantization scaling, the second quantization offset and the second quantization boundary, and a target quantization parameter is obtained; and the target video is subjected to AVS3 video encoding based on the target quantization parameter, and an encoding result is obtained. The visual quality of the AVS3 video encoding is improved.

A device for decoding a bitstream of encoded mesh data is configured to in response to determining that first transform parameters are overridden, infer a value of a second syntax element to be equal to a first value indicating that second transform parameters are present in the bitstream of encoded mesh data; in response to the value of the second syntax element being equal to the first value, determine the second transform parameters from a second syntax structure; in response to determining that the first quantization parameters are overridden, infer a value of a third syntax element to be equal to a first value indicating that second quantization parameters are present in the bitstream of encoded mesh data; and in response to the value of the third syntax element being equal to the first value, determine second quantization parameters from a third syntax structure.

The invention provides a video processing method, receiving end equipment and transmitting end equipment, and relates to the technical field of video transmission. The video processing method comprises the following steps: in response to a condition that a first health degree score of a network at a current moment is less than or equal to a first threshold value, discarding at least part of video coding frames in a buffer area, and sending a request for obtaining an instant decoding refreshing frame to a sending end; in response to receiving an instant decoding refresh frame, decoding the instant decoding refresh frame, the instant decoding refresh frame being obtained by encoding a target region in the original image frame based on a first quantization parameter and encoding a background region in the original image frame based on a second quantization parameter, the first quantization parameter does not change along with the change of the first health degree score, and the second quantization parameter is negatively correlated with the first health degree score. According to the method and the device, the delay of the video picture can be reduced, and the information of the target area can be clearly reserved, so that the stability, accuracy and safety of remote operation can be improved.

To solve the problem in which a quantization error is accumulated in each layer of a neural network and is finally increased to be output.SOLUTION: An information processing apparatus comprises: acquisition means that acquires second quantization information which includes information associated with quantization of a second network in the information processing apparatus that generates a deep layer learning model having a first network and the second network connected to the first network; determination means that generates first quantization information which is information associated with quantization of the first network on the basis of the second quantization information; and first quantization means in which first quantization based on the first quantization information is applied to the first network.SELECTED DRAWING: Figure 3

The invention provides a sheet material defect size quantification and motion condition detection method in rapid motion, which comprises the following steps: acquiring two-axis motion-generated eddy current signals of defects of experimental sheet materials with different motion speeds, lift-off distances and defect sizes, the two-axis motion-generated eddy current signals comprising parallel and vertical motion-generated eddy current signals; multiple characteristic parameters are obtained, and then a sample data set is constructed; a quantitative detection model is constructed based on a neural network, a parallel feature parameter input module in the quantitative detection model is connected with a fusion module through a first quantitative detection module, and a vertical feature parameter input module is connected with the fusion module through a second quantitative detection module. The output of each of the two quantitative detection modules comprises a movement speed, a lift-off distance and a defect size; training the model to obtain a target quantitative detection model; and obtaining a target two-axis motion-generated eddy current signal of the defect of the target thin plate material to be quantified, and outputting the defect size, the motion speed and the lift-off distance of the target thin plate material by using the target quantification detection model.

The present invention relates to the technical field of hybrid quantization, and in particular to a hybrid quantization method, device and electronic device for a large visual language model. The method comprises: inputting a constructed image encoder calibration set into a pre-established image encoder and a text encoder, selecting a quantization scheme from a pre-defined search space of hybrid precision quantization to quantize the image encoder after training, and calculating a first quantization error based on the outputs of the image encoder and the text encoder before and after quantization; inputting a multimodal calibration set into a large visual language model, and selecting a quantization scheme from the search space to perform hybrid precision quantization on the large visual language model, and calculating a second quantization error based on the outputs of the large visual language model before and after quantization; storing each quantization scheme and the corresponding quantization error in a hash table; and using a policy gradient-based reinforcement learning algorithm in combination with the hash table to optimize the strategy for generating a quantization scheme. The hash table is used to accelerate the search process of the quantization scheme and improve the optimization efficiency.

The application discloses a multi-array geomagnetic underground pipeline detection system and method. The system comprises one or more geomagnetic sensor arrays, each array comprising a group of three-axis fluxgate sensors, an analog-to-digital converter (ADC), a geomagnetic background field cancellation signal generation circuit and an analog differential summation node. For a first time sequence segment, the ADC generates first digital geomagnetic time sequence data with a first quantization bit width; the cancellation signal generation circuit generates an analog cancellation signal according to prior background field data, and cancels a known strong magnetic interference component from subsequent analog signals at the analog differential summation node; then the ADC is dynamically adjusted to a second quantization bit width smaller than the first quantization bit width; and the equivalent magnetic moment vector data of a weak magnetic target body is determined by calculating a scaling factor corresponding to a least square error in combination with an amplitude reference benchmark in the first digital geomagnetic time sequence data.

The present disclosure relates to an LCEVC-based enhancement layer coding method, device, equipment and medium, applied to the technical field of video coding, which can improve the coding quality of video coding. The method comprises: in the case of using temporal prediction for a to-be-coded enhancement layer sequence, obtaining the code rate of the current frame base layer corresponding to the current frame enhancement layer and the average code rate of the C frame base layer before the current frame base layer, the to-be-coded enhancement layer sequence comprising a plurality of enhancement layers, and the current frame enhancement layer being any frame in the plurality of enhancement layers; in the case that the code rate of the current frame base layer is less than or equal to the average code rate, coding the current frame enhancement layer based on a first quantization parameter; in the case that the code rate of the current frame base layer is greater than the average code rate, updating the quantization parameter of the current frame enhancement layer from the first quantization parameter to a second quantization parameter, and coding the current frame enhancement layer based on the second quantization parameter, the second quantization parameter being less than the first quantization parameter.

The application provides a binarization network training method and device based on gradient quantization, which comprises the following steps: in a full-precision model full-quantization training process taking a sample image as a training sample and taking a class label of the sample image as a training label, quantizing weights and input activation values of the full-precision model to obtain first quantization data with a target bit width; in a case of outputting activation value gradients by performing back propagation on the first quantization data with the target bit width, quantizing the activation value gradients to obtain second quantization data with the target bit width; and updating the activation value gradients and weight gradients according to the first quantization data and the second quantization data to obtain a trained binarization network. The method realizes binarization gradient training, effectively reduces the training power consumption of the model and the requirements for memory and computing power, and further improves the training efficiency and inference performance of the classification model in a complex scene.

The invention relates to the technical field of computer systems, and provides a fine-grained quantization matrix multiplication device and method based on systolic arrays, and the device comprises a first systolic array which is used for receiving element streams of a first quantization factor matrix and a second quantization factor matrix, and carrying out the fusion calculation of quantization factors according to the element streams of the quantization factors, generating a quantization factor product; the second systolic array is used for receiving element streams of the first data matrix and the second data matrix, and performing multiplication and accumulation calculation according to the element streams of the matrixes; each first processing unit in the first systolic array is configured to send a quantization factor product obtained by calculation to a corresponding anchor processing unit in the second systolic array according to a mapping proportion; and the anchor processing unit is configured to receive the quantization factor product and serve as a propagation source point, the quantization factor product is propagated in the second systolic array in the row direction and the column direction within the quantization block size range corresponding to the quantization factor product, and the calculation efficiency is improved.

The application discloses a language task processing method and electronic equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps: selecting target weight parameters according to task calibration data and an activation function type of an original task processing model; determining a fitting processing result of a quantization parameter according to each target weight parameter, a maximum weight parameter and the task calibration data; determining a first quantization error function and a second quantization error function carrying a scaling factor according to the fitting processing result, and obtaining a value of the scaling factor by minimizing the ratio of the two; performing scaling processing on each target weight parameter by using the scaling factor, and quantizing each scaling weight parameter based on a quantization precision parameter to obtain a task processing model used for executing a to-be-processed language task. The application can solve the problem that related technologies cannot guarantee high accuracy of a language task processing result on the basis of reducing resources used in a language task execution process, and effectively improve language task processing precision.

The present application provides an encoding device, a decoding device and a storage medium. The encoding device performs up-conversion and down-conversion on a first quantization matrix to generate a second quantization matrix, the first quantization matrix has a first number of rows and a first number of columns equal to the first number of rows to form a square matrix, the second quantization matrix has a second number of rows and a second number of columns different from the second number of rows to form a rectangular matrix; and, using the second quantization matrix to quantize the transform coefficients of the current block, wherein, in the up-conversion, the circuit generates the second quantization matrix by performing up-conversion in a first direction in such a way that one of the second number of rows and the second number of columns is greater than the first number of rows, the first direction is the horizontal direction; and, in the down-conversion, the circuit generates the second quantization matrix by performing down-conversion in a second direction in such a way that the other of the second number of rows and the second number of columns is less than the first number of rows, the second direction is different from the first direction, and the second direction is the vertical direction.

The invention provides a task processing method and device based on model quantization, equipment and a storage medium, and the method comprises the steps: updating a weight quantization coefficient and an activation quantization coefficient of an optimization unit according to the difference between first quantization output and first floating point output of the optimization unit in a Transform model; updating the weight quantization increment of the optimization unit according to the difference between the second quantization output and the second floating point output of the optimization unit; quantizing the weight parameter of the optimization unit according to the target quantization coefficient, and determining the weight quantization rounding direction of the optimization unit according to the target weight quantization increment; and performing forward reasoning calculation on the input data of the optimization unit according to the target quantization weight parameter of the optimization unit, and quantizing the input / output of the optimization unit according to the target activation quantization coefficient of the optimization unit. The method can improve the accuracy of task processing.

The invention provides a large language model quantization compression method based on limit quantization and multiple distillation, which comprises the following steps of: performing first quantization precision conversion: understanding a benchmark test set by using a natural language, taking a 32-bit BERT benchmark model as a teacher model, and performing quantization training to obtain an 8-bit intermediate model; a second quantization precision conversion step: understanding the benchmark test set by using a natural language, taking an 8-bit intermediate model as a teacher model, and performing quantization training to obtain a 1-bit target model; and configuring the target model in an edge server for natural language analysis processing of the resource-constrained edge environment. The method has the beneficial effects that the classic BERT model is taken as a target model, deep compression optimization is carried out on the BERT model, 32-bit storage bits are quantized and compressed to 1-bit storage bits, the size of the model is greatly reduced, meanwhile, a multiple distillation technology is used, and the practicability of the method in limited edge equipment is improved on the premise that the minimum loss of model performance is ensured.

A system includes code configured to cause a processor to obtain a video bitstream, the video bitstream including: a first quantization index value for a coefficient of a coded image; an offset value; a quantization step size that corresponds to the first quantization index value; a second quantization index value for another coefficient of the coded image, the second quantization index value being based on both the first quantization index value and the offset value and being greater than or equal to a predetermined threshold value; and a mode indicating whether the coded image is to be decoded in a lossy mode or a lossless mode, the mode being determined based on whether the first quantization index value is equal to a quantization index value associated with lossless coding, and whether the offset value is less than or equal to the quantization index value associated with the lossless coding.

To efficiently quantize rectangular blocks of various shapes.SOLUTION: The encoding device 100 includes a circuit 160 and a memory 162, wherein the circuit 160 generates a second quantization matrix for rectangular blocks by up-converting a first quantization matrix for square blocks only in a first direction and down-converting the first quantization matrix only in the first direction, quantizes a plurality of transform coefficients of a target block using the second quantization matrix, and encodes the plurality of quantized transform coefficients of the target block, and wherein the circuit 160 copies matrix elements of the first quantization matrix in the first direction in the up-conversion process; In the down-conversion process, the circuit 160 thins out the matrix elements of the first quantization matrix in the first direction and encodes only the information about the first quantization matrix out of the first quantization matrix and the second quantization matrix into a bit stream, where the first direction is the vertical direction.SELECTED DRAWING: Figure 31

An encoder which encodes a current block to be encoded in an image includes: a transformer which performs a primary transform from residuals of the current block to primary coefficients, determines whether to apply a secondary transform to the current block, and performs the secondary transform from the primary coefficients to secondary coefficients when the secondary transform is applied; a quantizer which calculates quantized primary coefficients by performing a first quantization on the primary coefficients when a secondary transform is not applied, and calculates quantized secondary coefficients by performing a second quantization different from the first quantization on the secondary coefficients when the secondary transform is applied; and an entropy encoder which generates an encoded bitstream by encoding either quantized primary coefficients or quantized secondary coefficients.