Abstract: A system having multiple devices that can host different versions of an artificial neural network (ANN) as well as different versions of a feature dictionary. In the system, encoded inputs for the ANN can be decoded by the feature dictionary, which allows for encoded input to be sent to a master version of the ANN over a network instead of an original version of the input which usually includes more data than the encoded input. Thus, by using the feature dictionary for training of a master ANN there can be reduction of data transmission.

Abstract: A computer system includes physical memory devices of different types that store randomly-accessible data in a main memory of the computer system. In one approach, data is stored in memory at one or more logical addresses allocated to an application by an operating system. The data is physically stored in a first memory device of a first memory type (e.g., NVRAM). The operating system determines an access pattern for the stored data. In response to determining the access pattern, the data is moved from the first memory device to a second memory device of a different memory type (e.g., DRAM).

Abstract: Systems, methods and apparatuses to throttle network communications for memory as a service are described. For example, a computing device can borrow an amount of random access memory of the lender device over a communication connection between the lender device and the computing device. The computing device can allocate virtual memory to applications running in the computing device, and configure at least a portion of the virtual memory to be hosted on the amount of memory loaned by the lender device to the computing device. The computing device can throttle data communications used by memory regions in accessing the amount of memory over the communication connection according to the criticality levels of the contents stored in the memory regions.

Abstract: Systems, methods and apparatuses to intelligently migrate content involving borrowed memory are described. For example, after the prediction of a time period during which a network connection between computing devices having borrowed memory degrades, the computing devices can make a migration decision for content of a virtual memory address region, based at least in part on a predicted usage of content, a scheduled operation, a predicted operation, a battery level, etc. The migration decision can be made based on a memory usage history, a battery usage history, a location history, etc. using an artificial neural network; and the content migration can be performed by remapping virtual memory regions in the memory maps of the computing devices.

Abstract: Systems, methods, and apparatus related to a memory system that manages an interface for a volatile memory device and a non-volatile memory device to control memory system power. In one approach, a controller evaluates a demand on memory performance. If the demand of a current computation task needed by the host is high, a DRAM device is powered-up to meet the demand. Otherwise, if the non-volatile memory device is adequate to meet the demand, the DRAM memory is partially or fully-powered down to save power. In another approach, a task performed for a host device uses one or more resources of a first memory device (e.g., DRAM). A performance capability of a second memory device (e.g., NVRAM) is determined. A controller of the memory system determines whether the performance capability of the second memory device is adequate to service the task.

Abstract: A computer system includes physical memory devices of different types that store randomly-accessible data in a main memory of the computer system. In one approach, data is stored in memory at one or more logical addresses allocated to an application by an operating system. The data is physically stored in a first memory device of a first memory type (e.g., NVRAM). The operating system determines an access pattern for the stored data. In response to determining the access pattern, the data is moved from the first memory device to a second memory device of a different memory type (e.g., DRAM).

Abstract: Systems, methods and apparatuses to intelligently migrate content involving borrowed memory are described. For example, after the prediction of a time period during which a network connection between computing devices having borrowed memory degrades, the computing devices can make a migration decision for content of a virtual memory address region, based at least in part on a predicted usage of content, a scheduled operation, a predicted operation, a battery level, etc. The migration decision can be made based on a memory usage history, a battery usage history, a location history, etc. using an artificial neural network; and the content migration can be performed by remapping virtual memory regions in the memory maps of the computing devices.

Abstract: A memory chip having a predefined memory region configured to store program data transmitted from a microchip. The memory chip also having a programmable engine configured to facilitate access to a second memory chip to read data from the second memory chip and write data to the second memory chip according to stored program data in the predefined memory region. The predefined memory region can include a portion configured as a command queue for the programmable engine, and the programmable engine can be configured to facilitate access to the second memory chip according to the command queue.

Abstract: Systems, methods and apparatuses of fine grain data migration in using Memory as a Service (MaaS) are described. For example, a memory status map can be used to identify the cache availability of sub-regions (e.g., cache lines) of a borrowed memory region (e.g., a borrowed remote memory page). Before accessing a virtual memory address in a sub-region, the memory status map is checked. If the sub-region has cache availability in the local memory, the memory management unit uses a physical memory address converted from the virtual memory address to make memory access. Otherwise, the sub-region is cached from the borrowed memory region to the local memory, before the physical memory address is used.

Abstract: Systems, methods and apparatuses of fine grain data migration in using Memory as a Service (MaaS) are described. For example, a memory status map can be used to identify the cache availability of sub-regions (e.g., cache lines) of a borrowed memory region (e.g., a borrowed remote memory page). Before accessing a virtual memory address in a sub-region, the memory status map is checked. If the sub-region has cache availability in the local memory, the memory management unit uses a physical memory address converted from the virtual memory address to make memory access. Otherwise, the sub-region is cached from the borrowed memory region to the local memory, before the physical memory address is used.

Abstract: Systems, methods and apparatuses to accelerate accessing of borrowed memory over network connection are described. For example, a memory management unit (MMU) of a computing device can be configured to be connected both to the random access memory over a memory bus and to a computer network via a communication device. The computing device can borrow an amount of memory from a remote device over a network connection using the communication device; and applications running in the computing device can use virtual memory addresses mapped to the borrowed memory. When a virtual address mapped to the borrowed memory is used, the MMU translates the virtual address into a physical address and instruct the communication device to access the borrowed memory.

Abstract: A memory chip having a predefined memory region configured to store program data transmitted from a microchip. The memory chip also having a programmable engine configured to facilitate access to a second memory chip to read data from the second memory chip and write data to the second memory chip according to stored program data in the predefined memory region. The predefined memory region can include a portion configured as a command queue for the programmable engine, and the programmable engine can be configured to facilitate access to the second memory chip according to the command queue.

Abstract: Systems, methods and apparatuses of distributed computing based on Memory as a Service are described. For example, a set of networked computing devices can each be configured to execute an application that accesses memory using a virtual memory address region. Each respective device can map the virtual memory address region to the local memory for a first period of time during which the application is being executed in the respective device, map the virtual memory address region to a local memory of a remote device in the group for a second period of time after starting the application in the respective device and before terminating the application in the respective device, and request the remote device to process data in the virtual memory address region during at least the second period of time.

Abstract: Systems, methods and apparatuses to accelerate accessing of borrowed memory over network connection are described. For example, a memory management unit (MMU) of a computing device can be configured to be connected both to the random access memory over a memory bus and to a computer network via a communication device. The computing device can borrow an amount of memory from a remote device over a network connection using the communication device; and applications running in the computing device can use virtual memory addresses mapped to the borrowed memory. When a virtual address mapped to the borrowed memory is used, the MMU translates the virtual address into a physical address and instruct the communication device to access the borrowed memory.

Abstract: Systems, methods and apparatuses of distributed computing based on Memory as a Service are described. For example, a set of networked computing devices can each be configured to execute an application that accesses memory using a virtual memory address region. Each respective device can map the virtual memory address region to the local memory for a first period of time during which the application is being executed in the respective device, map the virtual memory address region to a local memory of a remote device in the group for a second period of time after starting the application in the respective device and before terminating the application in the respective device, and request the remote device to process data in the virtual memory address region during at least the second period of time.

Abstract: Methods and apparatus of Exclusive OR (XOR) engine in a random access memory device to accelerate cryptographical operations in processors. For example, an integrated circuit memory device enclosed within a single integrated circuit package can include an XOR engine that is coupled with memory units in the random access memory device (e.g., having dynamic random access memory (DRAM) or non-volatile random access memory (NVRAM)). A processor (e.g., System-on-Chip (SoC) or Central Processing Unit (CPU)) can have encryption logic that performs cryptographical operations using XOR operations that are performed by the XOR engine in the random access memory device using the data in the random access memory device.

Abstract: A computer system includes physical memory devices of different types that store randomly-accessible data in a main memory of the computer system. In one approach, data is stored in memory at one or more logical addresses allocated to an application by an operating system. The data is physically stored in a first memory device of a first memory type (e.g., NVRAM). The operating system determines an access pattern for the stored data. In response to determining the access pattern, the data is moved from the first memory device to a second memory device of a different memory type (e.g., DRAM).

Abstract: An accelerator chip, e.g., an artificial intelligence (AI) accelerator chip, that can connect a system on a chip (SoC) and a memory chip. The accelerator chip can have a first set of pins configured to connect to the memory chip via wiring, as well as a second set of pins configured to connect to the SoC via wiring. The accelerator chip can be configured to perform and accelerate application-specific computations (e.g., AI computations) for the SoC, as well as use the memory chip as memory for the application-specific computations. For example, the accelerator chip can be an AI accelerator chip and the AI accelerator chip can be configured to perform and accelerate AI computations for the SoC, as well as use the memory chip as memory for the AI computations.

Abstract: A memory chip having a predefined memory region configured to store program data transmitted from a microchip. The memory chip also having a programmable engine configured to facilitate access to a second memory chip to read data from the second memory chip and write data to the second memory chip according to stored program data in the predefined memory region. The predefined memory region can include a portion configured as a command queue for the programmable engine, and the programmable engine can be configured to facilitate access to the second memory chip according to the command queue.

Abstract: A memory chip (e.g., DRAM) connecting a SoC and an accelerator chip (e.g., an AI accelerator chip). A system including the memory chip and the accelerator chip. The system can include the SoC. The memory chip can include first memory cells to store and provide computation input data (e.g., AI computation input data) received from the SoC to be used by the accelerator chip as computation input (e.g., AI computation input). The memory chip can include second memory cells to store and provide first computation output data (e.g., AI computation output data) received from the accelerator chip to be retrieved by the SoC or reused by the accelerator chip as computation input. The memory chip can also include third memory cells to store second computation output data (e.g., non-AI computation output data) related to non-AI tasks received from the SoC to be retrieved by the SoC for non-AI tasks.