The present invention relates to a dynamic memory management method which includes generating an N-dimensional memory address space in which coordinates are in a range of N natural numbers, the sum of which is the number of bits; and mapping a predetermined linear memory address region to an address region in the N-dimensional memory address space.

A processor includes a memory hierarchy, buffer, and a decompressor. The decompressor includes circuitry to read elements to be decompressed according to a compression scheme, parse the elements to identify literals and matches, and, with the literals and matches, generate an intermediate token stream formatted for software-based copying of the literals and matches to produce decompressed data. The intermediate token stream is to include a format for multiple tokens that are to be written in parallel with each other, and another format for tokens that include a data dependency upon themselves.

Data to be stored at a memory sub-system can be received from a host system. A portion of the host data that includes user data and another portion of the host data that includes system metadata can be determined. A mapping for a data structure can be received that identifies locations of the data structure that are fixed with respect to an encoding operation and locations of the data structure that are not fixed with respect to the encoding operation. The data structure can be generated for the user data and system metadata based on the mapping, and an encoding operation can be performed on the data structure to generate a codeword.

Apparatuses, systems, and methods related to memory pooling between selected memory resources are described. A system using a memory pool formed as such may enable performance of functions, including automated functions critical for prevention of damage to a product, personnel safety, and/or reliable operation, based on increased access to data that may improve performance of a mission profile. For instance, one apparatus described herein includes a memory resource, a processing resource coupled to the memory resource, and a transceiver resource coupled to the processing resource. The memory resource, the processing resource, and the transceiver resource are configured to enable formation of a memory pool between the memory resource and another memory resource at another apparatus responsive to a request to access the other memory resource transmitted from the processing resource via the transceiver.

Apparatuses, systems, and methods related to memory pooling between selected memory resources are described. A system using a memory pool formed as such may enable performance of functions, including automated functions critical for prevention of damage to a product, personnel safety, and/or reliable operation, based on increased access to data that may improve performance of a mission profile. For instance, one apparatus described herein includes a memory resource, a processing resource coupled to the memory resource, and a transceiver resource coupled to the processing resource. The memory resource, the processing resource, and the transceiver resource are configured to enable formation of a memory pool between the memory resource and another memory resource at another apparatus responsive to a request to access the other memory resource transmitted from the processing resource via the transceiver.

Methods are provided for creating objects in a way that permits an API client to explicitly participate in memory management for an object created using the API. Methods for managing data object memory include requesting memory requirements for an object using an API and expressly allocating a memory location for the object based on the memory requirements. Methods are also provided for cloning objects such that a state of the object remains unchanged from the original object to the cloned object or can be explicitly specified.

A compressed memory system of a processor-based system includes a memory partitioning circuit for partitioning a memory region into data regions with different priority levels. The system also includes a cache line selection circuit for selecting a first cache line from a high priority data region and a second cache line from a low priority data region. The system also includes a compression circuit for compressing the cache lines to obtain a first and a second compressed cache line. The system also includes a cache line packing circuit for packing the compressed cache lines such that the first compressed cache line is written to a first predetermined portion and the second cache line or a portion of the second compressed cache line is written to a second predetermined portion of the candidate compressed cache line. The first predetermined portion is larger than the second predetermined portion.

The technology disclosed partitions a dataflow graph of a high-level program into memory allocations and execution fragments. The memory allocations represent creation of logical memory spaces in on-processor and/or off-processor memories for data required to implement the dataflow graph. The execution fragments represent operations on the data. The technology disclosed designates the memory allocations to virtual memory units and the execution fragments to virtual compute units. The technology disclosed partitions the execution fragments into memory fragments and compute fragments, and assigns the memory fragments to the virtual memory units and the compute fragments to the virtual compute units. The technology disclosed then allocates the virtual memory units to physical memory units and the virtual compute units to physical compute units. It then places the physical memory units and the physical compute units onto positions in the array of configurable units and routes data and control networks between the placed positions.

A method for optimizing a layout of a tensor memory defines at least one hard constraint for allocating a plurality of input/output (I/O) vectors for reading and writing data for a task in the tensor memory. The at least one hard constraint is applied to determine one or more potential conflicts between the plurality of I/O vectors. One or more soft constraints aimed at mitigating the one or more potential conflicts between the I/O vectors may also be generated. The at least one hard constraint is applied in a maximum satisfiability (MaxSAT) solver. The one or more soft constraints may also be applied in the MaxSAT solver. The MaxSAT solver determines locations of the data in the tensor memory. The starting addresses of the input data to be read and of output data to be written by each of the I/O vectors are updated in the tensor memory.

In examples there is a computing device comprising a processor, the processor having a memory management unit. The computing device also has a memory that stores instructions that, when executed by the processor, cause the memory management unit to receive a memory access instruction comprising a virtual memory address; translate the virtual memory address to a physical memory address of the memory, and obtain permission information associated with the physical memory address. Responsive to the permission information indicating that metadata is permitted to be associated with the physical memory address, a check is made of a metadata summary table stored in the physical memory to check whether metadata is compatible with the physical memory address. Responsive to the check being unsuccessful, a trap is sent to system software of the computing device in order to trigger dynamic allocation of physical memory for storing metadata associated with the physical memory address.