13 results about "Channel quantization" patented technology

The invention discloses a dual-channel collaborative unknown signal reconstruction error control method, and belongs to the technical field of wireless communication. The method solves the problem that the existing method cannot give consideration to both the signal reconstruction precision and the information content contained in the signal. The invention provides a scheme of combining dual-channel collaborative quantization and error control of a receiving end, a dual-channel quantization mechanism realizes information complementation through different quantization characteristics, single-channel error accumulation is inhibited, and the signal detail retention capability is improved. And in combination with an interpolation processing method, estimating and compensating quantization information, optimizing reconstruction output, and completing high-fidelity signal recovery. According to the method, the reconstruction precision and fidelity of unknown signals can be remarkably improved, the robustness and adaptability of the system under the condition of lack of prior information are ensured, meanwhile, extreme dependence on the performance of a single-channel quantizer can be reduced, and then efficient balance between computing resources and reconstruction quality is achieved. The method can be applied to the field of wireless communication.

A large language model quantization method based on channel arrangement and activation adaptive smoothing belongs to the technical field of large language model compression and efficient inference. In order to solve the problem that the existing post-training quantization method is difficult to simultaneously consider the difference of different channel activation outliers, the group mode quantization has large dynamic range in the group, and the low bit weight and high bit activation alignment efficiency is low, which leads to the problem of precision decline and inference efficiency limitation, the scheme calibrates the maximum activation amplitude of the input channel by statistical data, divides the significant and non significant activation outlier channels according to the percentile threshold, and respectively uses the logarithmic domain and power function domain smoothing strategy to generate channel smoothing scaling factor, and fuses it into the model parameter to extract the weight multidimensional feature to construct the channel quantization sensitivity index and execute the input channel rearrangement, and further generate the joint quantization scaling factor to realize the integer domain alignment inference. It is suitable for low bit compression deployment and efficient inference scene of large language model in resource limited environment.

The invention discloses a reconfigurable intelligent surface assisted backscattering signal coding method and system, and solves the problems of low multi-signal superposition processing efficiency, low signal identification degree and low transmission efficiency in the prior art. Each quantized signal is input into one RIS unit; the method comprises the following steps: preprocessing an input quantized signal, dividing signals of K channels into two equalization subsets, and distributing different weights for the quantized signal of each channel; calculating the phase shift and backscattering information of each channel quantized signal in the subset by using the weight and the channel number K and combining the opening and closing condition of each RIS unit, and carrying out backscattering signal coding; and based on the obtained coded signal, combining gain in a signal transmission process to obtain a superposed signal. The problem of signal superposition in air computing is solved, the signal identification degree and the transmission efficiency are improved, and the complexity and the cost of an air computing system are reduced.

This invention discloses a method and system for evaluating the effect of wellbore grouting based on change attribution, relating to the field of grouting quality evaluation technology. The method includes: acquiring pre-grouting and post-grouting data for the same detection well; extracting baseline defect characteristics from the pre-grouting data and establishing a pre-grouting defect template; constructing a non-grouting reference response based on the pre-grouting defect template and combining the response patterns of stable sections and adjacent background changes; extracting measured response characteristics from the post-grouting data; calculating the grouting action and residual defect amount using dual-channel quantization based on the measured response characteristics, the non-grouting reference response, and the pre-grouting defect template; identifying change attribution based on the grouting action and residual defect amount to obtain initial attribution results; and performing section-level continuity optimization based on the initial attribution results to obtain the grouting effect evaluation results.

Systems, apparatuses and methods may provide technology for optimizing an inference neural network model that performs asymmetric quantization by generating a quantized neural network, wherein model weights of the neural network are quantized as signed integer values, and wherein an input layer of the neural network is configured to quantize input values as unsigned integer values, generating a weights accumulation table based on the quantized model weights and a kernel size for the neural network, and generating an output restoration function for an output layer of the neural network based on the weights accumulation table and the kernel size. The technology may also perform per-input channel quantization. The technology may also perform mixed-precision auto-tuning.

The application provides an offline model optimization method, device, equipment, storage medium and program product, and relates to the technical field of data processing. The offline model optimization method constructs weight distribution feature statistical results through each weight channel corresponding to each network layer in the to-be-optimized offline model, calculates the channel importance index and the target channel quantization factor corresponding to each weight channel based on the same data framework, realizes deep linkage and cooperation of weight quantization and channel pruning, further performs channel pruning and weight quantization optimization processing on the to-be-optimized offline model based on the channel importance index and the target channel quantization factor corresponding to each weight channel, can reduce the decision mismatch caused by the separation design of weight quantization and channel pruning, reduce the risk of deleting key weight channels by mistake, improve the adaptability of the reserved weight channels to low-precision representation, and significantly reduce the model precision loss, the model parameter quantity, the reasoning time consumption and the calculation amount without retraining.

A circuit for channel randomization based on time-interleaved ADC includes: a channel selection module for outputting M clock reception control signals and encoded N data reception control signals based on a main clock and a generated random number; a multi-phase clock distribution module for generating N multi-phase clocks according to a sampling main clock, redistributing the multi-phase clocks according to the clock reception control signals, and outputting M redistributed clock signals; a time-interleaved ADC module for outputting M output data and a corresponding number of channel quantization completion signals according to the redistributed clock signals; an adjustable delay module for setting a delay length for the data reception control signals; and a timing distribution control module for controlling, according to delayed data reception control signals and the channel quantization completion signals, the output data to be output sequentially in chronological order.

The invention discloses a quantification method of a model, a reasoning method of the model and electronic equipment. In the method, the first model is quantized based on the quantization parameter of the weight, the quantization parameter of the activation value, the first bias vector and the second bias vector, on one hand, the channel-by-channel quantization of the weight and the lexical element-by-lexical quantization of the activation value can be realized, and the quantization precision is relatively high; and on the other hand, the original bias vector is decomposed into two low-rank bias vectors, and the two low-rank bias vectors are spliced with the weight and the activation value respectively during quantization, so that the parameter quantity can be reduced, the memory occupation is reduced, and the acceleration effect of the quantization model is realized.

Systems, apparatuses and methods may provide technology for optimizing an inference neural network model that performs asymmetric quantization by generating a quantized neural network, wherein model weights of the neural network are quantized as signed integer values, and wherein an input layer of the neural network is configured to quantize input values as unsigned integer values, generating a weights accumulation table based on the quantized model weights and a kernel size for the neural network, and generating an output restoration function for an output layer of the neural network based on the weights accumulation table and the kernel size. The technology may also perform per-input channel quantization. The technology may also perform mixed-precision auto-tuning.

This invention proposes a method and apparatus for efficient vector quantization based on pre-quantization and post-correction, relating to the field of image data discrete compression and reconstruction technology. The method includes: encoding input data into a continuous representation using a pre-trained variational autoencoder; dividing the continuous representation into multiple channel groups using a multi-channel quantization strategy, assigning an independent codebook to each channel group and generating corresponding quantization features; constructing an EfficientViT-based post-corrector to optimize the quantization features, generating optimized features; and using a pre-trained variational autoencoder decoder to decode the optimized quantization features to obtain reconstructed data.

This disclosure relates to optimizing low-precision inference models for deployment in deep neural networks. Systems, apparatus, and methods provide techniques for performing asymmetric quantization for optimizing inference neural network models, achieved by: generating a quantized neural network, wherein the model weights of the neural network are quantized to signed integer values, and wherein the input layer of the neural network is configured to quantize input values ​​to unsigned integer values; generating a weight accumulation table based on the quantized model weights and the kernel size of the neural network; and generating an output recovery function for the output layer of the neural network based on the weight accumulation table and the kernel size. The technique also performs per-input channel quantization. The technique also performs mixed-precision autotuning.

The invention discloses a commodity information management method and system based on big data analysis. The method relates to the technical field of commodity information management, and comprises the following steps: multi-source data acquisition and synchronous quality quantification, data synchronous adaptive regulation and control, supply chain information penetration and risk quantification, and risk intelligent tuning and collaborative response. According to the method, a multi-source commodity information acquisition channel is established, the accuracy and timeliness of supplier service data synchronization are quantified, and whether the information visualization link is entered after self-adaptive regulation and control is judged according to the accuracy and timeliness; and then, obtaining supply chain information penetration and risk parameters, and judging whether to enter a supply chain collaboration link after intelligent adjustment and optimization so as to balance data, risks, resources and efficiency, thereby improving the commodity information management reliability and improving the commodity information management efficiency. The problem of low commodity information management reliability caused by islanding of the business process and the cooperation mode in the prior art is solved.

This present disclosure provides a communication method and apparatus used to adaptively determine CSI channel quantization parameters for EDs at different sensing environments, so that the feedback accuracy of the CSI may be improved. The method includes: receiving a reference signal; obtaining first channel state information by performing channel measurement based on the reference signal; transmitting a first channel state information report, where the first channel state information report includes quantized first channel state information that is quantized based on first channel quantization information, the first channel quantization information indicates a channel quantization parameter corresponding to a first sensing environment parameter set, and a first sensing environment of a ED corresponds to the first sensing environment parameter set.