249 results about "Encoding (memory)" patented technology

A system and method for persistent cognitive computation with temporally synchronized multimodal processing implements a geometric approach to artificial intelligence through typed latent entities within a dynamic manifold substrate. The system maintains a latent manifold incorporating heterogeneous data modalities where local curvature reflects semantic density and typed entities are stratified according to structural properties. Temporal synchronization coordinates asynchronous multimodal data streams through generation of temporal alignment fields within the manifold that preserve semantic coherence across modal boundaries. Type-aware geometric operations enforce operation legality based on entity type and local manifold geometry, enabling structured recombination, compression, and traversal while preventing semantic distortion. The system executes synchronized manifold reorganization during idle periods through coordinated optimization operations including perturbation analysis and topological surgery. This architecture enables persistent memory through geometric encoding where frequently accessed concepts develop high-curvature regions and cognitive patterns emerge from usage-based manifold evolution.

The invention provides a multi-modal semantic fusion image and text relevance dynamic analysis method and system, and belongs to the technical field of multi-modal data processing. The method comprises the steps of image and text data preprocessing, feature extraction, fusion semantic vector generation through a bidirectional cross attention mechanism, dynamic relevance score calculation through a time sequence attention long-short-term memory network and combined loss function end-to-end training. According to the method, accurate alignment of image and text features can be realized through a bidirectional cross attention mechanism, the cross-modal matching accuracy is improved, the dynamic correlation analysis capability is enhanced by using time sequence modeling, and the problems of insufficient feature coding suitability and limited time sequence modeling capability in the prior art are solved.

A computer-implemented method of training an encoder neural network of an autoencoder, comprising: receiving a data stream at the autoencoder, the autoencoder comprising an encoder neural network, a decoder neural network, a first memory layer, and a second memory layer; and incrementally training the encoder neural network. The incremental training comprises: performing an encoding process on the input data by the encoder neural network to obtain a latent representation of the input data; processing the encoded input data and encoded input data stored in the first memory layer from previous iterations of the training steps to create a memory representation; performing a decoding process on the latent representation; processing the decoded input data and the updated memory representation to refine the updated memory representation; and outputting the refined memory representation to the encoder neural network for use in a next training step.

A computer-implemented method includes receiving enrollment audio from a user comprising a wake word to be enrolled for the device, preprocessing the enrollment audio to obtain a vector representation along at least a feature dimension and a temporal dimension, inputting the extracted vector representation to a trained encoding model to generate an embedding representation of the enrollment audio, wherein the encoding model includes a plurality of mixing blocks, and wherein the feature dimension and the temporal dimension of an output of a first layer of each mixing block are flipped for inputting to a second layer of the mixing block, and storing the generated embedding representation in a memory for use in detecting input of the enrolled wake word.

Disclosed is an operation device. The operation device includes a memory storing at least one instruction; and a processor configured to execute the at least one instruction, and the processor, by executing the at least one instruction, may perform encoding or decoding for an approximate homomorphic ciphertext using a predetermined matrix having only a half of an element of a matrix corresponding to a canonical embedding function.

The invention relates to the technical field of intelligent medicine recommendation, and provides a medicine recommendation method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the time sequence coding processing of the current treatment information of a patient, and generating a semantic vector of the current treatment information; performing multi-message propagation processing on the heterogeneous medical graph based on a graph convolutional neural network to determine knowledge memory vectors of various medical entities, and determining knowledge vectors of the current doctor seeing information based on the knowledge memory vectors; determining fusion information according to the semantic vector and the knowledge vector; and inputting the fusion information into a drug recommendation model, carrying out probability calculation processing that new drugs are used and probability calculation processing that historical drugs are reserved and used on the fusion information, and based on the determined newly-added use probability of each drug, the reserved use probability of each historical drug and an adjacent matrix of drug interaction, carrying out drug recommendation. And determining the drug combination recommended to the patient. The safety of medicine combination is improved, and the accuracy of medicine recommendation is effectively improved.

An apparatus for generating text from an image may comprise: a memory configured to store at least one instruction; and a processor configured to execute the at least one instruction, wherein the processor is further configured to generate encoding information for an image based on the image and extract text information related to content of the image based on a degree of association with the encoding information.

A system and method for an adaptive network architecture utilizing dynamically-encoded agents. The system processes data through a base graph layer of interconnected computational nodes, a telemetry layer for real-time monitoring, and one or more agent layers composed of dynamically-encoded agents. These agents optimize encoding strategies, generate new agents, and prune inefficient agents based on network performance objectives. A telemetry layer continuously tracks network operations using adaptive kernel functions and topology-aware distance metrics. The system may dynamically adjust network structure and resource allocation, maintaining efficient operations through encoding optimization. By leveraging short-term and long-term memory systems, the system adapts over time, improving learning retention and responsiveness. Error detection and recovery mechanisms ensure network stability during agent generation and pruning. This approach enables real-time network adaptation, optimizing performance and efficiency across multiple layers while maintaining system resilience and stability.

The invention discloses a storage and calculation integrated array convolution acceleration device and method based on sparse coding, and belongs to the field of artificial intelligence chip design and digital signal processing. The device comprises an unpacking module, a packing module, a calculation reasoning module, a weight configuration module and a state control module. The unpacking module checks and decodes the received data packet, and distributes control information and data content to the weight configuration module or the calculation reasoning module; in a weight configuration mode, the weight configuration module converts an unpacking result into a parallel format, and controls the storage and calculation integrated array to be written into a sparse convolution kernel; in a reasoning mode, the calculation reasoning module decodes sparse excitation and drives the photoelectric storage and calculation integrated unit array to perform convolution calculation, and outputs a calculation result to the packaging module; and the packaging module packages the calculation result and the checksum packet header information and outputs the packaged calculation result and checksum packet header information to the outside. Through combination of a modular architecture and a sparse coding mode, the calculation efficiency is improved, and the input data volume is reduced at the same time.

A system and method for implementing persistent cognitive computation through geometric representation of thought in a dynamic latent manifold. The system encodes inputs into a curved space characterized by time-evolving metric tensors, compression pressure fields derived from Ricci curvature, and goal potential fields that shape attention flow. Cognition occurs through geodesic traversal of this manifold, with attention following paths that minimize cognitive action while balancing semantic density and goal relevance. A Cognitive Dynamics Engine maintains manifold geometry, computing optimal trajectories and managing thought bundle operations including consolidation, expansion, and higher-order abstraction. During idle periods, autonomous dreaming processes reorganize the manifold through perturbation, recombination, and topological surgery. This architecture enables persistent memory through geometric encoding, where frequently accessed concepts develop high-curvature regions and cognitive shortcuts emerge from usage patterns, transforming artificial intelligence from stateless computation to structured motion through shaped memory space.

This invention provides a method and system for constructing a cross-device, cross-modal user memory graph. The method includes acquiring multimodal data from multiple devices and applications, uniformly parsing it into standardized memory atomic events with timestamps, calculating causal relationships between adjacent events during real-time processing, linking highly correlated event pairs into instantaneous event chains, performing deep encoding of the event chains using an offline dual-stream temporal semantic embedding model, and inferring candidate graph substructures using a graph neural network. When fusing these substructures into the user's main graph, relationship conflicts are resolved through confidence comparison, retaining highly reliable results, identifying stable subgraphs, and using a multi-head attention graph summarization network to abstract the subgraphs into higher-order memory concept nodes, thereby achieving continuous optimization and updating of the graph.

An example memory system includes a memory device and a memory controller coupled to the memory device. The memory device may include memory cells having a memory bit count of multiple bits, the memory cells include first and second types of memory bits, the first type of memory bits are used to store valid data, the second type of memory bits are used to store first type of check data, and the first type of check data is obtained by performing error correction encoding on the valid data stored in the first type of memory bits. The memory controller is configured to: perform error correction on the valid data in which an error occurs by at least using the first type of check data in the second type of memory bits when the error occurs in reading the valid data in the first type of memory bits.

This application discloses a method and apparatus for managing video memory blocks, belonging to the field of data processing technology. The method includes: acquiring the occupancy status and access activity curves of video memory blocks within multiple consecutive address segments; for each consecutive address segment, performing frequency domain transformation and encoding based on the occupancy status and access activity curves of the video memory blocks within the segment to generate a video memory convolution spectrum characterizing the fragmentation degree and temporal access activity of that segment of video memory; determining candidate migration regions based on the video memory convolution spectra of each consecutive address segment; determining the differential tensors of candidate video memory blocks within the candidate migration regions; performing a comprehensive evaluation based on the differential tensors of all candidate video memory blocks under preset bandwidth and quality of service constraints to generate a video memory migration plan; and calling the underlying interface to execute the batch migration of candidate video memory blocks according to the video memory migration plan.

A three-dimensional memory array comprising a plurality of NAND strings. Each NAND string is configured to store a storage data having multiple bits and receive a searching input having multiple bits. Each bit of the storage data and the searching input is of a bit value “0”, a bit value “1”, a wildcard bit or an invalid bit. At least one bit of the storage data is configured as data bit(s), at least bit of the searching input is configured as search bit(s). All of the data bit(s) and the search bit(s) are of bit value “0” or bit value “1”. When the position of the data bit(s) in the storage data overlaps with the position of the search bit(s) in the searching input, the NAND string that stores the storage data is turned off; otherwise, the NAND string that stores the storage data generates an output current.

System (120) which features: a processor (260) that executes computer-executable components stored in memory (270), wherein the computer-executable components include: a compilation component (230) that encodes one or more qubits (140) according to a ring-shaped repetition code at a time after operations on the one or more qubits and before readout, wherein the computer-executable components further comprise a branch detection component (220) that detects a first chain of several auxiliary qubits associated with a specific qubit of the one or more qubits (140), a second chain of several auxiliary qubits associated with the specific qubit, and a marker qubit that combines the first chain of auxiliary qubits and the second chain of auxiliary qubits, wherein the encoding of the one or more qubits comprises encoding the specific qubit by: Encoding the first chain of auxiliary qubits using repetition encoding; and Encoding the second chain of auxiliary qubits using repetition coding.

The application discloses a video shadow detection method and device based on a dynamic prompt memory network, which can process confused shadows with the help of a projected shadow object, can aggregate time information without accumulating errors, and comprises a dynamic prompt module and a prompt-based memory module; the dynamic prompt module converts semantic information provided by a visual base model DINOv2 into a shadow mask when direct encoding of a shadow is difficult, local prompting focuses on semantic conversion and identifies a shadow position based on semantic information, and global prompting identifies a shadow boundary based on color and texture information; the memory module is used for solving deformation and long-term time consistency problems, preventing error accumulation, using local prompting as a time matching agent, reducing memory usage, and minimizing dependence on historical shadow masks; and the method can improve detection performance in a complex scene and maintain time consistency of results during long-term detection.

An encoding device (100) is provided with: a memory (152); and a circuit (151) capable of accessing the memory (152), the circuit (151), during operation, encodes a connection type pertaining to a connection relationship between a surface to be processed and an unprocessed surface by context-based arithmetic encoding for each of a plurality of surfaces constituting a three-dimensional grid, and, during encoding of a current connection type, which is the connection type to be encoded, encodes the current connection type to be encoded by context-based arithmetic encoding for each of the plurality of surfaces constituting the three-dimensional grid. The context to be applied to the arithmetic encoding is selected from a plurality of contexts using a previous connection type that is the connection type encoded prior to the current connection type.

The present invention relates to the field of compilation of source code files. Disclosed is a method for supporting the compilation of an ultra-large source code file on the basis of an ANTLR tool. In the method for supporting the compilation of an ultra-large source code file on the basis of an ANTLR tool, when traversing tokens corresponding to each row of statements, a visitor encodes the tokens and stores the encoded tokens in a temporary file, and a token memory set and an encoding memory are no longer retained; when the last statement is parsed and compiled, the temporary file is combined with other information to form a complete compiled file; the size of the total memory mainly depends on a memory occupied in an ANTLR parse tree after the compiled file is read into the memory; and a parsed storage structure contains only information of public sections such as header and vectorList, and a small amount of token and label information for jumping, such that the overall memory footprint is greatly reduced. When an ultra-large file is compiled, the memory footprint remains stable, thereby enabling smooth compilation.

A method, computer program product, and computing system for generating first encoded data by performing a first encoding of data included within each of a plurality of memory dies of a memory module using an exclusive-or (XOR) encoding process. Second encoded data is generated by performing a second encoding of the data included within each of the plurality of memory dies of the memory module and the first encoded data using a cyclic code encoding process. Error correction is performed on the data included within each of the plurality of memory dies of the memory module using the first encoded data, the second encoded data, an XOR decoding process, and a cyclic code error correction process.

A memory system including a memory; an encoding management circuit; and a memory access instruction decoding unit is provided. The memory access instruction decoding unit is configured to receive an instruction from a bus, identify a bad block in the memory, determine whether the instruction indicates access to the bad block. When the instruction indicates writing data to the bad block, the memory access instruction decoding unit outputs an address of the bad block and the data to the encoding management circuit to trigger the encoding management circuit to update the encoding management circuit. When the instruction indicates reading the bad block, the memory access instruction decoding unit outputs the address to and trigger the encoding management circuit to output simulation data of the bad block, wherein the encoding management circuit obtains the simulation data based on the address.

The invention discloses a device for acute abdominal disease recognition and a storage medium. The apparatus comprises: a processor; the device realizes the following operations: constructing a knowledge graph for acute abdominal disease identification; performing multi-dimensional sub-graph division on the knowledge graph, forming a corresponding coherent text, and obtaining a multi-dimensional sub-graph set; calculating the correlation of each sub-graph, and extracting a target sub-graph set with the maximum correlation from the multi-dimensional sub-graph set; inputting the clinical information and the target sub-atlas into a language encoder for text encoding to obtain text features; based on the image data, performing image encoding by using an image encoder, and extracting image features; and based on the text features and the image features, a multi-mode decoder is used for decoding, and an identification result of acute abdominal disease identification is obtained. By using the scheme of the invention, accurate and credible identification results and decision support can be provided for clinic.

This invention discloses an adaptive frame abstraction interface management method and system for multiple encoding formats, relating to the field of data processing technology. The method includes: constructing a format configuration state diagram and a page transition time sequence; identifying the page transition time interval for corresponding format jumps; extracting structural difference parameters of the preceding and following formats and making them dimensionless to obtain fragmentation expectation parameters; making the actual time dimensionless and adding it to the expectation parameters to obtain reassembly resistance parameters; calculating the pre-allocated memory capacity based on the resistance parameters and the real-time bit rate; and performing physical isolation and decoding context preloading. This invention solves the problem of latency accumulation and screen stuttering caused by insufficient underlying memory reassembly and state machine switching during dynamic switching of multiple encoded video streams, achieving smooth transitions and low-latency decoding of video streams.

The application discloses a kind of dual-dimension cognitive function quantitative evaluation system and method based on multi-modal physiological signal.System includes brain electrical signal input module, force touch task presentation module, force feedback operation terminal, synchronous alignment unit, dual-dimension feature extraction module and cognitive evaluation engine.When user executes continuous tracking task, system uses operation failure timestamp as anchor point, jointly extracts ERP latency and frequency band power ratio, constructs attention stability index and resource consumption rate;In memory task, the intensity of encoding, extraction accuracy and scene consistency index are quantified.Cognitive evaluation engine integrates features into attention and memory comprehensive index, and maintains task in the most sensitive cognitive interval through adaptive difficulty adjustment mechanism.The present application embeds neurophysiological evaluation in real operation scene, significantly improves the ecological validity, sensitivity and individual adaptability of evaluation.

An encoding method, applied to the field of encoding technologies, may be used for reducing the delay and reducing the decoding memory, and includes: obtaining a data signal including a plurality of information bits; and performing encoding process on the data signal to obtain an encoded signal. An encoding block in the encoded signal includes a first encoding sub-block and at least one second encoding sub-block. The first encoding sub-block includes first component codewords and second component codewords, and all information bits constitute an information bit matrix. Each second encoding sub-block includes a first check matrix, which includes first check bit sets each determined based on bits of a first bit matrix with same side lengths, and the first check bit set and the bits of the first bit matrix constitute a third component codeword.

Various embodiments describe an apparatus, a method, and a computer program product. An example apparatus includes at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the apparatus at least to perform: encoding an input picture by using a first encoder or first encoding parameters; encoding the input picture by using a second encoder or second encoding parameters; generating a first reconstructed picture based on the encoding of the input picture by using the first encoder or the first encoding parameters; and generating a second reconstructed picture based on the encoding of the input picture by using the second encoder or the second encoding parameters.

The invention discloses a memory detection and check method and system based on coding, and the method comprises the steps: setting a dual-refresh constant for each memory region in a memory corresponding to each processor, and each group of dual-refresh constants comprises two refresh constants which are opposite to each other; performing periodic memory refreshing on the memory by using double refreshing constants, wherein the period comprises an odd period and an even period; wherein in the odd period, one refreshing constant in the double refreshing constants is used for carrying out memory refreshing on the corresponding memory area, and in the even period, the other refreshing constant in the double refreshing constants is used for carrying out memory refreshing on the corresponding memory area; reading back the memory area after the memory is refreshed, and calculating a check word based on data obtained by reading back; and checking the check word obtained by calculation. According to the memory detecting and checking method and system based on coding, the condition that a specific bit in the memory is wrong can be effectively detected, and the reliability and safety of the system are improved.

Provided in the embodiments of the present application are a decoding method, an encoding method, a training method, a decoder and an encoder. The decoding method comprises: decoding a code stream of a point cloud, and determining first tensor data, wherein the first tensor data comprises a feature value, on at least one first channel, of attribute information of the point cloud, which attribute information has been subjected to feature extraction; on the basis of the first tensor data, determining second tensor data by using a first neural network, wherein the first neural network and a second neural network that is used by an encoder are used for forming a generator of a generative adversarial network, the second tensor data comprises a feature value, on at least one second channel, of reconstructed attribute information of the point cloud, and the at least one first channel is different from the at least one second channel; and on the basis of the second tensor data, determining the reconstructed attribute information of the point cloud. By means of the decoding method, when both the calculation complexity and the memory occupation of a decoder are taken into account, point cloud attribute information of a good quality can be reconstructed, i.e., the decoding performance of the decoder can be improved.

The encoding device (100) comprises a circuit (a1) and a memory (a2) connected to the circuit (a1). In operation, the circuit (a1) is configured to determine each of a plurality of sub-pictures constituting a picture such that at least one pixel included in the sub-picture is included in a consistent cropping window, encode configuration information representing configurations of the determined plurality of sub-pictures, and encode each of the plurality of sub-pictures.

The application discloses a large language model weight compression method, an inference operation method, a system and a medium, and belongs to the technical field of large language model weight compression and inference operation optimization. The method comprises the following steps: acquiring a weight matrix of a large language model and constructing an exponential high-frequency window and a benchmark value, classifying and encoding each weight data into a fixed-length code word and splitting the fixed-length code word into an independent bitmap, synchronously generating a compact value stream and a rollback value stream, and packing the compact value stream and the rollback value stream into weight compression data; loading the weight compression data, generating a state mask and a channel prefix mask in a register based on the bitmap, calculating a reading offset by using a population count instruction, reading a compressed data segment from a corresponding data stream, reconstructing weight data in the register in combination with the code word, and finally directly inputting the weight data into a matrix multiplication unit for operation; therefore, by implementing the application, the problem of efficiency reduction caused by control flow divergence and redundant memory access in GPU inference of existing variable-length encoding can be solved, and the model inference efficiency under lossless compression can be improved.

An encoding device (100) is provided with a memory (152) and a circuit (151) capable of accessing the memory (152), the circuit (151), during operation, encodes a connection type by context-based arithmetic encoding (S1410) for each of a plurality of surfaces constituting a three-dimensional grid, selects a context (S1411) using a first previous connection type during encoding of a current connection type, and performs encoding of the current connection type using a second previous connection type during encoding of the current connection type. When the first previous connection type is shifted by the angle type, the context is selected according to the number of more than one angle type continuously encoded up to the first previous connection type.