5 results about "Dynamic memory management" patented technology

A computer system implements a unified framework integrating an adaptive elastic funnel (AEF) with a convergent intelligence fabric (CIF) for flexible and contextualized multi-agent AI and human collaboration at scale. The system provides a universal multi-modal key-value subsystem for sharing partial computations across agents, implements a hybrid greedy / non-greedy placement strategy for dynamic memory management, orchestrates dynamic computational workflows and tensor workflows using hierarchical tensor-fragment scheduling, enables cross-agent orchestration with policy-based privacy preservation, and incorporates quantum-resistant secure memory enclaves. The architecture supports continuous learning without catastrophic forgetting, compositional reasoning across modalities, and secure task execution in distributed environments. This integration enables unprecedented computational efficiency, secure collaboration, and adaptive intelligence in high-dimensional decision-making environments while supporting incremental adoption through modular interfaces.

This application discloses a memory management method and electronic device, relating to the field of computer technology. The method includes configuring the memory of any controller in a target cluster based on its current memory management parameters, running preset test cases on the controller to obtain performance indicators under the current memory configuration, determining the fitness function value of the controller under the current memory configuration, and then determining the controller's historically optimal memory management parameters. If the current iteration count has not reached an iteration count threshold, the memory management parameters are updated based on the target update speed, and iteration continues based on the updated memory management parameters. If the current iteration count reaches the iteration count threshold, the controller is configured with appropriate memory based on its historically optimal memory management parameters. This achieves dynamic memory management of the controller, resulting in improved memory utilization.

A memory controller is configured to receive segment pointers that identify physical memory devices and slices for associated segments. The memory controller is configured to identify a most-full first one of the physical memory devices, to identify a most-empty second one of the physical memory devices, and to identify one of the segments in the most-full first one of the physical memory devices. The memory controller is configured to move a slice from the identified one of the segments in the most-full first one of the physical memory devices to the most-empty second one of the physical memory devices. The memory controller is configured to update one of the segment pointers for the identified segment. The memory controller is configured to update one of the segment pointers for the identified segment by removing a previous segment pointer to the most-full first one of the physical memory devices.

A computer system implements a unified framework integrating an adaptive elastic funnel (AEF) with a convergent intelligence fabric (CIF) for multi-agent AI collaboration. The system provides a multi-layer key-value subsystem for sharing computations across GPU partitions, applying hybrid placement strategies for dynamic memory management. It unifies physical GPU sub-allocation and virtual GPU time-slicing through a common abstraction layer while maintaining isolation through policy-based multi-tenancy. A predictive resource orchestration system forecasts computational needs, while speculative data scheduling proactively manages memory access patterns. The architecture includes risk-based scheduling for uncertain workloads, test-time compute scaling, and federated learning for edge collaboration. This framework delivers computational efficiency, security, and adaptive intelligence in high-dimensional environments while supporting incremental adoption through modular interfaces.

The application provides a long text intelligent processing dynamic memory management optimization method and system, and relates to the field of artificial intelligence, which comprises the following steps: dividing semantic units and extracting feature vectors, constructing a dependency propagation graph to quantify the correlation between units. When the memory occupation is too high, the target unit is released according to the dependency depth and storage occupation, and the semantic offset of the target unit and the core associated unit is stored. When accessed again, the target unit is reconstructed by superimposing the offset and the anchor unit vector. The method efficiently manages long text data under limited memory, balances the processing performance and resource consumption.