Abstract: Systems, methods and apparatuses to provide memory as a service are described. For example, a borrower device is configured to: communicate with a lender device; borrow an amount of memory from the lender device; expand memory capacity of the borrower device for applications running on the borrower device, using at least the local memory of the borrower device and the amount of memory borrowed from the lender device; and service accesses by the applications to memory via communication link between the borrower device and the lender device.

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 of Artificial Neural Network (ANN) applications implemented via Memory as a Service (MaaS) are described. For example, a computing system can include a computing device and a remote device. The computing device can borrow memory from the remote device over a wired or wireless network. Through the borrowed memory, the computing device and the remote device can collaborate with each other in storing an artificial neural network and in processing based on the artificial neural network. Some layers of the artificial neural network can be stored in the memory loaned by the remote device to the computing device. The remote device can perform the computation of the layers stored in the borrowed memory on behalf of the computing device. When the network connection degrades, the computing device can use an alternative module to function as a substitute of the layers stored in the borrowed memory.

Abstract: Systems, methods and apparatuses to throttle network communications for memory as a service. In one exemplary embodiment, 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 of Artificial Neural Network (ANN) applications implemented via Memory as a Service (MaaS) are described. For example, a computing system can include a computing device and a remote device. The computing device can borrow memory from the remote device over a wired or wireless network. Through the borrowed memory, the computing device and the remote device can collaborate with each other in storing an artificial neural network and in processing based on the artificial neural network. Some layers of the artificial neural network can be stored in the memory loaned by the remote device to the computing device. The remote device can perform the computation of the layers stored in the borrowed memory on behalf of the computing device. When the network connection degrades, the computing device can use an alternative module to function as a substitute of the layers stored in the borrowed memory.

Abstract: Systems, methods, and apparatus related to data recovery in memory devices. In one approach, a memory device encodes stored data. The memory device reads a codeword from a storage media and determines that a number errors in the codeword exceeds an error correction capability of the memory device. The errors are due, for example, to one or more stuck bits. In response to this determination, one or more data patterns are written to the storage media at the same address from which the codeword is read. The data patterns are read to identify bit locations of the stuck bits. The identified locations are used to correct bit errors of the read codeword that correspond to the identified locations. The corrected code word is sent to a host device (e.g., which requested data from the memory device using a read command).

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: Systems, methods and apparatuses to provide memory as a service are described. For example, a borrower device is configured to: communicate with a lender device; borrow an amount of memory from the lender device; expand memory capacity of the borrower device for applications running on the borrower device, using at least the local memory of the borrower device and the amount of memory borrowed from the lender device; and service accesses by the applications to memory via communication link between the borrower device and the lender device.

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 apparatus related to data recovery in memory devices. In one approach, a memory device encodes stored data. The memory device reads a codeword from a storage media and determines that a number errors in the codeword exceeds an error correction capability of the memory device. The errors are due, for example, to one or more stuck bits. In response to this determination, one or more data patterns are written to the storage media at the same address from which the codeword is read. The data patterns are read to identify bit locations of the stuck bits. The identified locations are used to correct bit errors of the read codeword that correspond to the identified locations. The corrected code word is sent to a host device (e.g., which requested data from the memory device using a read command).

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 memory chip having a first set of pins configured to allow the memory chip to be coupled to a first microchip or device via first wiring. The memory chip also having a second set of pins configured to allow the memory chip to be coupled to a second microchip or device via second wiring that is separate from the first wiring. The memory chip also having a data mover configured to facilitate access to the second microchip or device, via the second set of pins, to read data from the second microchip or device and write data to the second microchip or device. Also, a system having the memory chip, the first microchip or device, and the second microchip or device.

Abstract: A computer system includes physical memory devices of different types that store randomly-accessible data in memory of the computer system. In one approach, access to memory in an address space is maintained by an operating system of the computer system. A virtual page is associated with a first memory type. A page table entry is generated to map a virtual address of the virtual page to a physical address in a first memory device of the first memory type. The page table entry is used by a memory management unit to store the virtual page at the physical address.

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 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 first set of pins configured to allow the memory chip to be coupled to a first microchip or device via first wiring. The memory chip also having a second set of pins configured to allow the memory chip to be coupled to a second microchip or device via second wiring that is separate from the first wiring. The memory chip also having a data mover configured to facilitate access to the second microchip or device, via the second set of pins, to read data from the second microchip or device and write data to the second microchip or device. Also, a system having the memory chip, the first microchip or device, and the second microchip or device.

Abstract: A computer system includes physical memory devices of different types that store randomly-accessible data in memory of the computer system. In one approach, access to memory in an address space is maintained by an operating system of the computer system. A virtual page is associated with a first memory type. A page table entry is generated to map a virtual address of the virtual page to a physical address in a first memory device of the first memory type. The page table entry is used by a memory management unit to store the virtual page at the physical address.

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 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 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).