1532 results about "Memory module" patented technology

The invention provides an agent-based large language model retrieval enhancement generation system and method, and the system comprises a planning layer which is used for receiving user query, carrying out the multi-round iterative decomposition of a complex task through a task planning agent, and generating an atomic query or a direct response; the execution layer is used for executing the atomic query generated by the planning layer in parallel, calling a search module to obtain external knowledge base data, and caching an intermediate result through a memory module; the answer detection module is used for performing multi-dimensional detection on the generated result, including preference, accuracy, integrity and logicality; the dynamic decision-making module is used for adaptively adjusting a subsequent retrieval strategy and a task planning process according to a detection result and user feedback; and a cross-layer interaction mechanism enables the planning layer and the execution layer to realize collaborative optimization through context sharing and iterative feedback.

The invention discloses a submarine cable risk dynamic assessment method and system based on multi-modal deep learning, and belongs to the field of marine infrastructure operation and maintenance. Aiming at the problems of incomplete data coverage, unreal generated scene, low evaluation reliability and the like in the prior art, the method comprises the following steps of: 1) constructing a multi-source heterogeneous data set containing six types of data including geology, ocean, ships, biology and the like, and realizing data alignment by adopting space-time grid coding; 2) designing a physical constraint generative adversarial network, and generating risk scene data conforming to a fluid mechanics law through a Navier-Stokes equation constraint; 3) creating a hierarchical space-time fusion network (HST-Transform), and combining CNN spatial feature extraction, a time sequence attention mechanism and a dynamic memory module to realize multi-modal fusion; according to the method, the detection rate of rare risk events is increased by 62%, the evaluation accuracy rate reaches 91.7%, the false alarm rate is reduced by 34% compared with a traditional method, and submarine cable breakage accidents can be effectively prevented.

The invention discloses an intelligent traffic data analysis and decision generation system based on multi-model dynamic collaboration, and the system comprises a multi-mode collection module which collects voice, text, video and structured data and loads a traffic knowledge graph; the feature processing module is used for extracting semantic, time sequence and spatial features and generating a fusion vector; the semantic modeling module is used for generating an intention vector in combination with the knowledge graph and the interaction record; the dynamic routing module selects an edge or cloud model according to the intention vector to generate a query statement; the Prompt memory module fuses the historical template and the alignment parameters to generate a query draft; the query verification module is used for executing semantic and structure verification and outputting a correction statement; the query execution module generates a response vector; the causal interpretation module generates an interpretation vector; and the response generation module outputs a multi-granularity result according to the user role. According to the method, the cooperative processing capability and semantic analysis precision of complex traffic query are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-level memory collaborative decision-making method, device, equipment and medium, and the method comprises the steps: obtaining environment observation data, and updating a multi-level memory module; retrieving the memory generation candidate communication content; high-level actions are extracted according to the task target, and a to-be-selected action plan is formed; reasoning and analyzing the action plan and the communication content through a language model, and determining whether to send; if so, incorporating into a to-be-executed action set; and generating a final plan based on the action plan and the action set, and decomposing the final plan into basic operation instructions to interact with the environment. According to the method, the environment, history and instructions are processed through the reasoning ability of the language model in combination with multi-level memory, the collaborative plan and the communication strategy of the intelligent agent are optimized in the environment with limited communication and incomplete information, and the decision consistency and the task execution efficiency are improved.

The invention discloses a knowledge graph reasoning method based on dynamic rule perception memory. The method aims at solving the problems that an existing neural combination rule learning method is insufficient in expression ability and prone to splitting global semantics and local relation modes. The method comprises the following steps: introducing a lightweight dynamic relationship memory module, and executing self-attention on all relationships in a knowledge graph to capture global semantics; meanwhile, local relation interaction features are extracted through convolution, semantic fusion is carried out through a Transform encoder with multi-head attention and relative position coding, and unified and parallel-computing high-quality combination representation is constructed for each pair of relations in parallel. And meanwhile, a closed high-quality reasoning path is generated in combination with bidirectional breadth-first search. Through global semantic and local interactive collaborative modeling, the extendibility is ensured, the understanding of global semantics is enhanced, and the reasoning accuracy is improved. Global and local relation dependence is fused, an interpretable reasoning path is generated, and reasoning accuracy, expandability and robustness are improved.

The invention relates to the technical field of natural language processing, in particular to a multi-modal interactive intelligent NPC dialogue intention matching method and system. The method comprises the following steps: collecting multi-modal data in real time, and generating multi-modal semantic features through sub-modal preprocessing; a cross-modal fusion module based on semantic association is adopted to integrate each modal semantic feature, and an intention candidate set is generated based on semantic context representation and in combination with a semantic analysis module and a predefined intention template; through a continuous semantic learning mechanism and an interactive memory module, in combination with a Bayesian updating method, tracking user intention change in real time, dynamically adjusting the confidence of each intention in the intention candidate set, and screening a final intention; constructing an NPC semantic cognition model, and performing semantic check on user input and an NPC dialogue state through semantic consistency analysis; generating a dialogue strategy in combination with the final intention and a decision engine, and outputting synchronous response content; according to the invention, the precision of intelligent dialogue intention dynamic matching is improved.

The invention relates to the technical field of storage control, in particular to a memory control method, which comprises the following steps: acquiring a target logic address in response to a reading request from a host system; determining a data access mode associated with the target logic address in real time based on the historical access information, wherein the data access mode at least comprises a continuous mode, a random mode and a hotspot mode; according to the determined data access mode, triggering a data prefetching strategy and a flash translation layer query strategy matched with the access mode; obtaining target data from a memory module based on a data prefetching strategy and a flash translation layer query strategy; and scheduling the cache resources and the input / output bandwidth according to a predetermined resource management strategy, and returning the target data to the host system. The prefetching accuracy and the cache utilization rate are improved.

The invention provides a vector core module of an artificial intelligence chip and an operation method of the vector core module, which are used for improving the efficiency of the vector core module in an application situation that a vector core is a host and a tensor core module is a slave. The vector core module comprises a vector core instruction execution pipeline, a tensor core instruction processing pipeline and an instruction scheduling unit. An instruction scheduling unit performs instruction classification to distinguish vector core instructions and tensor core instructions from a thread bundle. In response to the thread bundle including a vector core instruction, the instruction scheduling unit sends the vector core instruction to a vector core instruction execution pipeline. The vector core instruction execution pipeline executes the vector core instruction and stores an execution result in the memory module. In response to the thread bundle including a tensor core instruction, the instruction scheduling unit sends the tensor core instruction to a tensor core instruction processing pipeline. The tensor core instruction processing pipeline processes the tensor core instruction and sends a processing result to the tensor core module.

The invention discloses a consumer feedback analysis method based on a multi-agent model, and the method comprises the steps: firstly sampling and generating consumer multi-dimensional feature data, and expanding the data into a semantic portrait through a large model; therefore, an intelligent agent containing a memory module is constructed, and part of the intelligent agent is set as an opinion leader. Commodity attributes, propaganda and competitive products are input into each agent, and initial evaluation is independently output; and then multiple rounds of viewpoint interaction are carried out in the same social evaluation area, the opinion leader has a higher sampled weight, and memory and comments are updated. And after simulation is finished, updating evaluation is generated again and compared with the initial evaluation, attitude changes are analyzed, all data are summarized, and a visual comprehensive report is output. According to the method, accurate simulation and feedback analysis of consumer behaviors are realized by combining a large language model and a multi-agent simulation test technology, and the defects of a traditional method in the aspects of consumer portrait dynamics, agent adaptability, interaction complexity, data dependence and the like are overcome.

The invention belongs to the technical field of target tracking, and discloses a multi-target tracking method based on long and short term trajectory association. The method aims at solving the problems of precision limitation, track breakage, frequent identity switching and the like caused by insufficient collaborative optimization of detection and tracking tasks in a complex scene and limited long-term track modeling capability in an existing method. The invention provides a multi-target tracking method based on long and short term trajectory association. The method comprises the following steps: generating a target detection frame and detection query through a detection module based on YOLOX; the MOTR-ConvNext network dynamically updates the track query and predicts the target motion information; a track query memory module is used for storing and updating a historical track; the detection frame and the prediction frame are subjected to IOU matching through a matching module, and short-term association is achieved; cross-frame correlation is calculated for unmatched detection through a history backtracking module, and long-term correlation is achieved; and finally, combining the two association results to obtain a complete tracking result. According to the method, the tracking precision can be remarkably improved in a complex scene, and higher robustness is achieved in the aspect of target identity maintenance.

Direct bonded stack structures for increased reliability and improved yields in microelectronics are provided. Structural features and stack configurations are provided for memory modules and 3DICs to reduce defects in vertically stacked dies. Example processes alleviate warpage stresses between a thicker top die and direct bonded dies beneath it, for example. An etched surface on the top die may relieve warpage stresses. An example stack may include a compliant layer between dies. Another stack configuration replaces the top die with a layer of molding material to circumvent warpage stresses. An array of cavities on a bonding surface can alleviate stress forces. One or more stress balancing layers may also be created on a side of the top die or between other dies to alleviate or counter warpage. Rounding of edges can prevent stresses and pressure forces from being destructively transmitted through die and substrate layers. These measures may be applied together or in combinations in a single package.

Embodiments of the present application relate to the technical field of computer storage, and provide a resource allocation method and apparatus, an electronic device, and a storage medium. The method comprises: in response to a memory acquisition request sent by a target host, a memory management unit in a resource allocation system acquiring demanded memory capacity and a memory starting address carried by the memory acquisition request; on the basis of a memory allocation unit in the resource allocation system, selecting from among at least two memory modules a target memory module matching the demanded memory capacity; and on the basis of a memory address router in the memory allocation unit and the memory starting address, performing address translation on the target memory module, and allocating to the target host the target memory module having undergone address translation, thereby achieving dynamic resource allocation of the target host. In this way, the interaction between a memory management unit and a memory allocation unit ensures rapid allocation of idle memory modules in a system, thereby improving the utilization rate of memory resources, and reducing costs of usage of the memory resources.

The invention provides a memory management method and a memory controller, which are applied to a storage device configured with a memory module. The method comprises the following steps: acquiring a continuity state table, identifying a target mapping table according to a data access instruction and the continuity state table, acquiring a continuity state of the target mapping table, judging whether a continuous section mapping table is used or not according to the continuity state, and if so, loading the continuous section mapping table to a buffer memory, and if not, loading the target mapping table to the buffer memory, and obtaining the physical address according to the loaded mapping table to access the memory module. According to the method, the continuity of the entity address in the mapping table is evaluated, and the optimal mapping table processing mode is dynamically selected, so that the data access performance is effectively improved, and the use efficiency of the buffer memory is optimized.

The disclosed embodiments relate to a system including a connection element for producing a wired connection between a first unit and a second unit, the connection element comprising a diagnostic module; wherein the diagnostic module comprises: a receiving interface, which is set up to receive data from the first unit over a wired data connection, a memory module, which is set up to store the data received from the first unit, and a read-out interface, which is set up to output the data stored by the memory module over a wireless data connection. The disclosed embodiments furthermore relate to a system including the first unit with the connection element directly connected to the first unit, as well as a system with the first unit and the second unit. The disclosed embodiments finally also relate to a method for producing a wired connection.

The invention relates to the technical field of artificial intelligence, can be applied to the medical field and the financial science and technology field, and discloses a dialogue method and device based on a memory scheduling framework, equipment and a medium, which are applied to a bank virtual customer service scene or an intelligent triage and pre-inquiry scene. Obtaining a target dialogue page from all dialogue pages of the current dialogue chain in the short-term memory queue; a target topic segment and a target dialogue page are retrieved from the medium-term memory module, and target feature information is retrieved from the long-term personalized memory module; constructing a structured cue word, and generating a target response based on the structured cue word; storing the newly constructed dialogue page in a short-term memory queue; if the short-term memory queue is full, removing the dialogue page from the head of the queue; updating the topic segment in the medium-term memory module based on the removed dialogue page; and updating the long-term personalized memory module according to the popularity of the topic segment in the updated middle-term memory module. The dialogue response accuracy is improved.

The invention provides a memory management method and a memory controller. The method comprises the following steps: acquiring a plurality of virtual blocks of a memory module and corresponding error bit statistical information, evaluating the health condition of each virtual block, and selecting a target virtual block with a poorer health condition from the virtual blocks; and executing enhanced block mapping operation on the target virtual block, and mapping the entity blocks in the target virtual block into enhanced blocks in pairs. When the data is written, the data is written into the two entity blocks of the enhancement block in parallel; and during data reading, selecting one of the two entity blocks in turn according to a preset reading strategy for reading. Through dynamic enhancement block mapping and an alternate reading strategy, additional data protection is provided in the aging stage of the storage device, the service life of the device is prolonged, and meanwhile, dynamic balance between data security and storage capacity is achieved.

The invention provides a memory management method and a memory controller, which are applied to a storage device configured with a memory module, the memory module is provided with a plurality of chips, each chip is provided with a plurality of planes, and each plane is provided with a plurality of entity blocks. The method comprises the following steps: acquiring a chip enabling number of a memory module; selecting a target allocation method from a plurality of allocation methods according to the chip enabled number, the plurality of allocation methods including a first allocation method based on a plurality of chips and a second allocation method based on a plurality of planes; and dividing the plurality of entity blocks into a plurality of super blocks based on a target allocation method. Thus, the allocation strategy of the super blocks is optimized through the target allocation method, the utilization rate of the entity blocks in the memory module is improved, and meanwhile the garbage collection efficiency is improved.

The invention discloses an intelligent data center energy scheduling optimization method based on machine learning. The method comprises the following steps: acquiring data to form a multivariable time sequence; the improved PatchTST model calls a multi-level memory interaction structure to establish short-term memory and long-term memory, and an attention constraint energy consumption memory module is introduced to output prediction features; inputting the prediction features into a decoding layer of the improved PatchTST model; constructing an energy scheduling multi-objective optimization problem, and generating an equipment control instruction set by adopting an NSGA-III algorithm; collecting an actual execution result to form feedback data; performing deviation evaluation and strategy updating on feedback data through an energy self-feedback correction process; and judging whether the energy consumption prediction error and the energy scheduling deviation meet a preset termination condition or not. The method is suitable for intelligent energy scheduling and operation efficiency optimization in multi-energy coupling scenes such as an intelligent data center.

Embodiments of the present application provide a memory system, a method and apparatus for adjusting memory resources, an electronic device, and a non-transitory readable storage medium. The memory system includes a first motherboard, and a processor and a high-speed interconnection channel interface that are integrated on the first motherboard; a first adapter board, and a high-speed interconnection channel interface, a chip and a memory module that are integrated on the first adapter board; and at least one connecting cable; the at least one connecting cable is used to establish a physical link between the high-speed interconnection channel interface of the first motherboard and the high-speed interconnection channel interface of the first adapter board, and used to convert the high-speed channel interconnection signal into a processor internal dedicated bus signal transmitted by a physical link based on a peripheral component interconnect express; and the chip of the first adapter board is used to acquire the converted processor internal dedicated bus signal, and convert the processor internal dedicated bus signal into a double data rate memory signal. By the connecting cable, the high-speed channel interconnection signals are converted into IPB resources to expand DRAM resources.

The invention provides a power system load online prediction method based on a space-time dynamic graph, and belongs to the technical field of time series prediction, and the method comprises the following steps: S1, power system time series data collection; s2, constructing a space-time dynamic graph model, wherein a main body of the space-time dynamic graph model is composed of a time domain convolutional network (TCN) and a graph convolutional network (GCN); s3, constructing an online time domain convolution module; s4, constructing an online graph convolution module which comprises a matrix polynomial, a graph drift sensing mechanism and a graph memory module; the method aims to construct a time series online prediction model based on a space-time dynamic graph by using power system load data, the whole model captures space-time correlation of the power system load data through a time domain convolutional network TCN and a graph convolutional network GCN, a dynamic adjacency matrix is generated based on a matrix polynomial in a GCN module, and the dynamic adjacency matrix is predicted and trained online. And modeling the evolution trend of the transformer association mode to improve the accuracy of online prediction of the time series of the power system.

The invention discloses a manufacturing method of a reconfigurable LPCAMM storage device, and relates to the technical field of production of storage devices, and the manufacturing method of the reconfigurable LPCAMM storage device realizes comprehensive improvement of technical performance, production efficiency and commercial value through an innovative modular hierarchical architecture and process collaborative design. On the basis that the compact characteristic of a traditional LPCAMM is kept, through the dual effects of mechanical guiding and elastic compensation, the sub-board assembly precision is improved, through the collaborative design of a self-compensation contact array of the detachable storage sub-board and a heat dissipation strengthening layer (S200), the ultra-long plugging service life is achieved, and through the combination of three-dimensional microstructure heat dissipation and the phase change heat storage technology, the phase change heat storage performance is improved. The working temperature of the device is reduced, the thermal resistance value is reduced, the thermal management bottleneck of a high-density storage module is broken through, and a complete solution with high reliability, maintainability and cost effectiveness is provided for large-scale application of the LPCAMM technology in emerging markets such as cloud computing terminals and industrial Internet of Things equipment.

The invention discloses a 23T4M anti-radiation TCAM storage unit circuit which comprises a pre-charging module, an RH reading circuit module, a programmable logic module, a reconfigurable magnetic tunnel junction storage module, a discharging module and an output module. In the RH reading circuit module, the grid electrode of a transistor N7 is connected with the drain electrode of a transistor N8, the grid electrode of the transistor N8 is connected with the drain electrode of the transistor N7, a cross coupling structure is formed, a node S0 and a node S1 are controlled by a node Q and a node QB, meanwhile, the node S0 and the node S1 control the charging and discharging paths of the node Q and the node QB respectively, and after any node is overturned by high-energy particles, the charging and discharging paths of the node Q and the node QB are controlled by the node S0 and the node S1. All the nodes are maintained or charged to recover to the original logic level. According to the method, data upset at a single sensitive node can be completely resisted, and meanwhile, the search error rate of the TCAM is improved.

The invention discloses a real-time data receiving and transmitting system and method based on a kernel bypass, and the method comprises the steps: processing the received data when the data receiving and transmitting system receives the data, and achieving the high-level data with complete semantics; updating a write pointer through atomic operation, and writing high-level data into a lockless annular queue in the shared memory module; the upper-layer application module reads the lock-free annular queue in the shared memory; when the data receiving and transmitting system transmits data, upper-layer data to be transmitted is transmitted to the shared memory module, and a write pointer is updated; the upper layer data in the shared memory is read in a polling or interruption mode, a read pointer is updated, the upper layer semantic data is segmented according to the format, a UDP data packet is obtained, the UDP data packet is written into a network card in the polling or interruption mode, and data sending is achieved. Through the data receiving and transmitting system disclosed by the invention, microsecond-level ultra-low delay of data certainty is realized, and efficient decoupling and stable throughput are realized.

A method for processing faulty memory module and apparatus, and an electronic device and a non-transitory readable storage medium are provided. The method for processing faulty memory module includes: acquiring a first power-down command, the first power-down command being used for instructing a baseboard management controller of a memory resource pool to perform power-down processing on a faulty memory module in the memory resource pool; and in response to the first power-down command, when a first memory module in the memory resource pool is in a fault state and is allowed to be powered down, sending a second power-down command to a target memory expander controller to which the first memory module belongs, the second power-down command being used for instructing the target memory expander controller to perform power-down processing on the first memory module.

The invention provides a body navigation method, device and equipment based on brain inspiration structured spatial memory, and relates to the technical field of navigation.The method comprises the steps that a work memory module is used for determining a structured spatial memory module for searching a navigation instruction according to the complexity of the received navigation instruction, and the structured spatial memory module is used for searching the navigation instruction; the structured spatial memory module comprises at least one of a landmark memory module and a cognitive map module; responding to the navigation instruction, and performing hierarchical retrieval by using a structured spatial memory module of the navigation instruction to obtain a candidate coordinate set of the target; the candidate coordinate set comprises a plurality of candidate coordinates; determining an exploration sequence based on the confidence score of each candidate coordinate and the distance between each candidate coordinate and the coordinate of the current position of the body agent; the exploration sequence represents the exploration sequence of the candidate coordinates; and controlling the body agent to execute navigation operation based on the exploration sequence. According to the method, the robust space cognition and self-adaptive navigation capability of the intelligent agent with the body in a complex environment is realized.

A system for collision avoidance by an autonomous vehicle (AV) is disclosed. The system includes a sensor system to detect objects in the AV's proximity. The system also includes a pedestrian trajectory prediction (PTP) system with an input module to receive sensor data, including heatmap prior information about past pedestrian trajectories. A memory module stores the data, and a probabilistic motion model (PMM) module uses a dynamic polar occupancy grid to represent the pedestrian's predicted trajectory. The PMM calculates occupancy probabilities based on angular motion (related to heading) and radial motion (related to speed). A processing module combines these probabilities to predict the pedestrian's future position. If an imminent collision is detected, the system generates a warning signal output to the AV's user interface. This system enhances collision avoidance by combining past data and real-time sensor data with probabilistic trajectory predictions.

A system includes a substrate comprising a first circuit. The system also includes an integrated circuit formed in a first die disposed on the substrate. The integrated circuit includes at least a processor, a controller, and a first memory interface. The first memory interface is located in a first edge of the first die and is configured to couple to the first circuit. The system also includes a first buffer circuit formed in a second die disposed on the interposer substrate adjacent to the first edge of the first die. The first buffer circuit includes a second memory interface configured to couple to the first connection circuit. The system further includes multiple memory modules disposed on the second die. Each of the multiple memory modules at least partially share the second memory interface to communicate with the integrated circuit.

The invention discloses a robot and an intelligent interaction method thereof, and relates to the technical field of robots, and the robot comprises an instruction obtaining module which is used for converting external instruction information into text information; the artificial intelligence module is used for performing vectorization processing and similarity comparison on the text information and keywords in a preset task list through a large model to obtain a conventional target task and an unconventional target task; the task decomposition module is used for decomposing the conventional target task or the unconventional target task into a plurality of sub-tasks and determining solutions matched with the sub-tasks according to the types of the sub-tasks; the task execution module is used for supervising and controlling execution of each subtask; the memory module is used for storing key data and a knowledge graph database in the execution process of each subtask; and the planning decision module is used for controlling the operation of the modules. According to the invention, man-machine interaction and autonomous planning and intelligent control of the robot body can be realized.

The invention relates to a multi-threshold 16T anti-radiation SRAM (Static Random Access Memory) storage unit circuit and a memory, a storage module in the circuit is of a bistable structure formed by two cross-coupled phase inverters formed by PMOS (P-channel Metal Oxide Semiconductor) transistors, and two storage nodes are formed; the redundant storage module adopts NMOS (N-channel metal oxide semiconductor) transistors to form a bistable structure, and two redundant nodes are formed to serve as redundant backups of the storage module; when the read-write operation control module reads or writes data, a word line is activated, and the data is read from the storage node and the redundant node or written into the storage node and the redundant node through the switched-on transistor; and the bit line driving module is used for driving the bit lines during reading and writing operations. According to the circuit, by introducing redundant nodes and a feedback mechanism, efficient resistance and recovery of single-particle and multi-node upset are achieved, the anti-radiation capacity is improved, the static noise margin, the read-write speed and the power consumption performance are optimized, and the circuit is suitable for the reliable data storage requirement in the high-radiation environment.

The invention relates to a GPU memory access simulation system and method, a storage medium and electronic equipment. The GPU memory access simulation system comprises a GPU multi-thread simulation module, a page calculation module, a shared lock dynamic distributor and a GPU memory module. The page calculation module is configured to respond to a memory access request initiated by any thread in the GPU multi-thread simulation module and determine an accessed memory page based on the memory access request; and the shared lock dynamic allocator is configured to allocate one or more locks corresponding to the accessed memory pages to each thread based on the accessed memory pages corresponding to the memory access requests of each thread, so that all the threads initiating the memory access requests can access the GPU memory module according to the respective allocated locks. According to the method, one or more locks corresponding to the accessed memory pages are respectively allocated to each thread based on the accessed memory pages corresponding to the memory access requests of each thread through a shared lock dynamic allocation mechanism, so that the simulation efficiency of GPU memory access simulation is improved.