51 results about "Scalable system" patented technology

The invention discloses a multi-language data analysis-oriented extensible system, method, equipment and medium, and relates to the technical field of multi-language analysis, the multi-language data analysis-oriented extensible system comprises a grammar analysis module used for analyzing a query language and generating a language-specific syntax tree structure after analysis; the grammar adaptation module is used for converting a language-specific grammar tree structure into an abstract grammar tree AST in a uniform format; the abstract syntax tree processing module injects a permission control strategy, a field desensitization rule and an alarm exception expression into the AST; the business processing module is used for executing business-related rule check and label injection; the query plan generation module is used for constructing a logic query path and a corresponding distributed execution scheme; and the task scheduling module issues the distributed execution scheme to the corresponding execution node. According to the method, by constructing a unified multi-language grammar analysis framework and a standardized abstract syntax tree AST representation mechanism, compatibility and fusion of multiple query languages such as SQL, SPL and natural language are achieved.

The invention relates to a system and method for implementing a scalable system. A multi-chip system (100) and structure for modular extension is described. In some embodiments, bonding strips (150) are used to couple adjacent chips (102, 104). For example, a communication bar (150) may be used to couple a logic chip (104), and a memory bar (150) may be used to couple a plurality of memory chips (102) to the logic chip (104).

Several embodiments of scalable image processing systems and methods are disclosed herein whereby color management processing of source image data to be displayed on a target display is changed according to varying levels of metadata.

Several embodiments of scalable image processing systems and methods are disclosed herein whereby color management processing of source image data to be displayed on a target display is changed according to varying levels of metadata.

Several embodiments of scalable image processing systems and methods are disclosed herein whereby color management processing of source image data to be displayed on a target display is changed according to varying levels of metadata.

A test and measurement device is described, comprising one or more detection devices configured to receive an event from a corresponding event generator. The test and measurement device also includes a transmitter configured to receive the event from the one or more detection devices and transmit the event. The test and measurement device also includes one or more comparators configured to receive the event from the batch transmitter and forward the event to a corresponding event detector based on a comparison of an event identifier and a detector identifier.

PROVIDING VIDEO QUALITY WITH IMPROVED IMAGE FIDELITY SOLUTION: Some embodiments of scalable image processing systems and methods are disclosed herein in which the color management processing of source image data to be displayed on a target display (236) is varied according to different levels of metadata (204).SELECTED DRAWING: Figure 2A

Several embodiments of scalable image processing systems and methods are disclosed herein whereby color management processing of source image data to be displayed on a target display is changed according to varying levels of metadata.

The present disclosure proposes a bus-based fully automatic three-dimensional reconstruction system for non-cooperative ship targets, which belongs to the field of image processing technology. The system includes: a system bus layer and a functional module layer, wherein the functional module layer contains multiple functional modules for realizing data processing at different stages of the three-dimensional reconstruction process of non-cooperative ship targets; the system bus layer is connected to each module in the functional module layer via a data I / O interface, which is used to call the selected module from the functional module layer to obtain the corresponding data for processing and store the corresponding processing results. The present disclosure introduces the design concept of bus technology. Through the design of modular plug-ins, it can expand the universality of the system, improve the flexibility of the system, and effectively realize the fully automatic three-dimensional reconstruction of reconnaissance images of non-cooperative ship targets on the sea surface.

An operationally unintrusive and scalable system for measurement and transfer of pressure and temperature data from inside a fluid flow tubular such as a subsea pipeline to an existing remote site such as an infrastructure or a subsea vehicle that does not need to penetrate the fluid flow tubular's wall.

Several embodiments of scalable image processing systems and methods are disclosed herein whereby color management processing of source image data to be displayed on a target display is changed according to varying levels of metadata.

The application discloses a self-evolution multi-agent event semantic understanding system and method based on a dynamic scalable model, which comprises a multi-agent cooperation framework modeling module, a demand understanding and task planning module, a capability verification module, a model dynamic expansion module, a task execution module and a completion module connected in sequence, wherein the multi-agent cooperation framework modeling module comprises a central control type multi-agent, a task execution type multi-agent and a model expansion type multi-agent. The method focuses on a news event semantic understanding scene, constructs a dynamic scalable system architecture through a multi-agent cooperation mechanism, introduces a capability verification link to optimize a task execution process, and realizes on-demand iteration of model capability by using an active learning framework, so that adaptive evolution of agent capability is realized, thereby solving the problems of model rigidity and poor adaptability of a traditional event semantic understanding system in a complex news event scene, and significantly improving the dynamic adaptability and accuracy of news event semantic understanding.

This invention discloses a flexible control method for digital twin manufacturing systems addressing uncertainties in the production process, belonging to the field of intelligent manufacturing system technology. The method includes: statistically analyzing the time of the four stages of perception, analysis, decision-making, and execution; determining the advance prediction time by combining system complexity and simulation speed; constructing an scalable system state transition matrix; performing accelerated simulation based on real-time states and injecting uncertain events according to trigger probabilities to obtain multiple future states; selecting the top n high-probability states in descending order of occurrence probability for parallel decision-making; binding decision instructions with corresponding states and issuing them in advance; and executing the corresponding instructions through state matching when the physical system reaches the predicted time, achieving flexible control. This invention can improve the consistency and robustness of "virtual control of the real" in digital twin systems under uncertain conditions and is applicable to multi-level digital twin systems such as machine tools, robots, production lines, and workshops.

Several embodiments of scalable image processing systems and methods are disclosed herein whereby color management processing of source image data to be displayed on a target display is changed according to varying levels of metadata.

The present disclosure relates to scalable systems and methods for detecting, labeling, and protecting sensitive data in natural language processing (NLP) environments. This includes NLP applications in artificial intelligence (AI) systems, such as language models (LMs) and generative AI (GenAI). More particularly, the present disclosure introduces a hierarchical, context-aware labeling mechanism that is optimized using an LM in conjunction with machine learning (ML) techniques to ensure the utility-preserving effective protection of sensitive data with, for example, minimal false positives and false negatives and / or optimal precision and recall (e.g., in terms of an F1 Score).

Several embodiments of scalable image processing systems and methods are disclosed herein whereby color management processing of source image data to be displayed on a target display is changed according to varying levels of metadata.

Several embodiments of scalable image processing systems and methods are disclosed herein whereby color management processing of source image data to be displayed on a target display is changed according to varying levels of metadata.

Several embodiments of scalable image processing systems and methods are disclosed herein whereby color management processing of source image data to be displayed on a target display is changed according to varying levels of metadata.

A large language model elastic inference system based on dynamic layer skipping and stateless layer partitioning includes: a layer partitioning deployment module, a stateless coordination module, a dynamic layer skipping module, a GPU lease scheduling module, an elastic state management module, and a layer skipping-aware scaling module. This invention divides the model layer into independently deployable sub-model services and externalizes hidden states and KV caches as explicit state references, ensuring that each sub-model call remains stateless at the token level. Simultaneously, it generates layer skipping maps based on the input-output similarity of attention and feedforward network sub-layers, and combines GPU leases, shared memory / Redis state management, KV prefetching, and HQQ quantization to transform algorithmic sparsity into schedulable, reusable, and scalable system resources. This transforms large language model inference from a persistent whole model instance into a microservice chain that can be independently deployed, scheduled layer-by-layer, and has its state restored across replicas, thereby improving deployment flexibility and resource utilization in scenarios with limited GPU memory and sudden load spikes.

This disclosure provides a system (100) and a method (600) for parsing and decoding large amounts of diagnostic, logging, and tracking (DLT) data. The system (100) comprises a splitter module (108) and a parser module (112). The splitter module (108) receives a stream of a plurality of DLT files (102) from a storage system (104), processes each of the plurality of DLT files (102) across a predetermined number of cores of a storage node, and splits each of the plurality of DLT files (102) into a plurality of DLT messages (110). The parser module (112) receives the multitude of DLT messages (110) from the splitter module (108) via the storage system (104), decodes the multitude of DLT messages (110) into a human-readable format (114) via the storage nodes and stores the decoded DLT messages (114) in the storage system (104).

The invention relates to the technical field of agricultural data analysis and mining, in particular to an agricultural data assembly line type intelligent analysis and mining system, which adopts the technical scheme that automatic construction and execution of an agricultural data analysis process are realized through an intelligent engine driven by metadata and guided by knowledge, and the system can intelligently recommend analysis operators and dynamically assemble an assembly line. And an analysis path is adaptively adjusted based on data state change, and finally automatic conversion from original data to knowledge discovery is realized. Deep fusion of domain knowledge and data driving is realized, agricultural professional knowledge is converted into a form which can be understood by a machine through semantic tags and ability portraits, an SC-MPARank hybrid sorting model is provided, advantages of symbol constraints and a neural network are combined, interpretable intelligent recommendation is provided, a dynamic and evolvable analysis pipeline is constructed, and the analysis efficiency is improved. The recommendation effect is continuously optimized through a feedback learning closed loop, a decoupling and extensible system architecture is designed, and a new operator can be conveniently integrated into the system.

A method of verifying connectivity in a circuit design, includes, in part, receiving a netlist of the circuit design; designating a plurality of destination nodes associated with the netlist; for each of the plurality of destination nodes, identifying one or more source nodes that are traversed from the destination node; for each source node identified as traversed from the destination node: transforming the netlist by including a first multiplexer having a first input terminal receiving a first variable logic value and an output terminal coupled to the source node; and enabling the first multiplexer to pass the first variable value to the destination node from the source node in order to check for connectivity between the source node and the destination node.

The invention relates to a memory extensible system and method based on a fine-grained static computational graph, in the system, a structural unit is used for analyzing the structure of a neural network model into the fine-grained static computational graph taking a computational node as a basic unit, and obtaining training state information of the neural network model; the model planning unit is used for calculating to obtain a calculation balance division point and a memory balance division point of a stage node according to the fine-grained static calculation graph and the training state information, and enumerating calculation nodes in a closed interval formed by the calculation balance division point and the memory balance division point; determining an optimal segmentation point to divide the neural network model into a left sub-calculation graph and a right sub-calculation graph, and processing the sub-calculation graphs until a target stage number is reached; and the optimization unit is used for evaluating the overhead cost of the application memory exchange and re-calculation technology based on the cost model after the model division is completed so as to select a video memory optimization scheme.

Several embodiments of scalable image processing systems and methods are disclosed herein whereby color management processing of source image data to be displayed on a target display is changed according to varying levels of metadata.

Techniques are disclosed related to a scalable system on a chip (SOC). In some embodiments, a system includes a plurality of processor cores, a plurality of graphics processing units, a plurality of peripheral circuits, and a plurality of memory controllers configured to support scaling of the system using a unified memory architecture.

An addition of one or more storage memory devices including a compute express link (CXL) is detected by two or more storage controllers of a storage system. The CXL is configured to provide access to the one or more storage memory devices for the two or more storage controllers. The two or more storage controllers store at least one of data or metadata in the one or more storage memory devices. The two or more storage controllers one or more storage system operations using the at least one of data or metadata.

PROVIDING VIDEO QUALITY WITH IMPROVED IMAGE FIDELITY SOLUTION: Some embodiments of scalable image processing systems and methods are disclosed herein in which the color management processing of source image data to be displayed on a target display (236) is varied according to different levels of metadata (204).SELECTED DRAWING: Figure 2A