Abstract: An operation method of a memory controller, which is configured to control a memory module including a plurality of memory devices and at least one error correction code (ECC) device, is provided. The method includes reading a data set including user data stored in the plurality of memory devices and ECC data stored in the at least one ECC device, based on a read command and a first address, and writing uncorrectable data in a memory area, which is included in each of the plurality of memory devices and the at least one ECC device and corresponds to the first address, when an error of the user data is not corrected based on the ECC data.

Abstract: An electronic device includes a graphic processor and a memory device. The graphic processor includes an artificial neural network engine that makes an object recognition model learn by using learning data and weights to provide a learned object recognition model. The memory device, divides a feature vector into a first sub feature vector and a second feature vector, and performs a first calculation to apply the second sub feature vector and the weights to the learned object recognition model to provide a second object recognition result. The artificial neural network engine performs a second calculation to apply the first sub feature vector and the weights to the learned object recognition model to provide a first object recognition result and provides the first object recognition result to the memory device. The second calculation is performed in parallel with the first calculation.

Abstract: An electronic device includes a graphic processor and a memory device. The graphic processor includes an artificial neural network engine that makes an object recognition model learn by using learning data and weights to provide a learned object recognition model. The memory device divides a feature vector into a first sub feature vector and a second feature vector, and performs a first calculation to apply the second sub feature vector and the weights to the learned object recognition model to provide a second object recognition result. The artificial neural network engine performs a second calculation to apply the first sub feature vector and the weights to the learned object recognition model to provide a first object recognition result and provides the first object recognition result to the memory device. The second calculation is performed in parallel with the first calculation.

Abstract: A memory system includes a nonvolatile memory module and a first controller configured to control the nonvolatile memory module. The nonvolatile memory module includes a volatile memory device, a nonvolatile memory device, and a second controller configured to control the volatile memory device and the nonvolatile memory device. The first controller may be configured to transmit a read request to the second controller. When, during a read operation according to the read request, normal data is not received from the nonvolatile memory device, the first controller may perform one or more retransmits of the read request to the second controller without a limitation on a number of times that the first controller performs the one or more retransmits of the read request.

Abstract: A memory system includes a processor that includes cores and a memory controller, and a first semiconductor memory module that communicates with the memory controller. The cores receive a call to perform a first exception handling in response to detection of a first error when the memory controller reads first data from the first semiconductor memory module. A first monarchy core of the cores performs the first exception handling and the remaining cores of the cores return to remaining operations previously performed.

Abstract: An electronic device includes a graphic processor and a memory device. The graphic processor includes an artificial neural network engine that makes an object recognition model learn by using learning data and weights to provide a learned object recognition model. The memory device divides a feature vector into a first sub feature vector and a second feature vector, and performs a first calculation to apply the second sub feature vector and the weights to the learned object recognition model to provide a second object recognition result. The artificial neural network engine performs a second calculation to apply the first sub feature vector and the weights to the learned object recognition model to provide a first object recognition result and provides the first object recognition result to the memory device. The second calculation is performed in parallel with the first calculation.

Abstract: A memory system includes a nonvolatile memory electrically connected to a data bus, a DRAM electrically connected to the data bus, and a memory controller configured to drive the DRAM as a cache memory and the nonvolatile memory as a main memory and to synchronize data of a cache line with data of the nonvolatile memory in units of cache units based on a dirty flag. The DRAM is configured to load data of the cache line that caches data stored in the nonvolatile memory and to store the dirty flag, which indicates whether a cache unit is dirty, in units of cache units, where a size of each cache unit is smaller than a size of the cache line.

Abstract: An electronic device includes a graphic processor and a memory device. The graphic processor includes an artificial neural network engine that makes an object recognition model learn by using learning data and weights to provide a learned object recognition model. The memory device, divides a feature vector into a first sub feature vector and a second feature vector, and performs a first calculation to apply the second sub feature vector and the weights to the learned object recognition model to provide a second object recognition result. The artificial neural network engine performs a second calculation to apply the first sub feature vector and the weights to the learned object recognition model to provide a first object recognition result and provides the first object recognition result to the memory device. The second calculation is performed in parallel with the first calculation.

Abstract: A storage module includes a dynamic random access memory (DRAM) device, a nonvolatile memory device, and a high-speed buffer memory. An method of operating the storage module includes copying target data stored in the nonvolatile memory device to the high-speed buffer memory in response to an external device entering a page fault mode, receiving a first refresh command from the external device, and, in response to the first refresh command, performing a first refresh operation associated with the DRAM device and moving the target data copied to the high-speed buffer memory to the DRAM device during a first refresh reference time.

Abstract: A storage module includes a dynamic random access memory (DRAM) device, a nonvolatile memory device, and a high-speed buffer memory. An method of operating the storage module includes copying target data stored in the nonvolatile memory device to the high-speed buffer memory in response to an external device entering a page fault mode, receiving a first refresh command from the external device, and, in response to the first refresh command, performing a first refresh operation associated with the DRAM device and moving the target data copied to the high-speed buffer memory to the DRAM device during a first refresh reference time.

Abstract: An electronic device includes a graphic processor and a memory device. The graphic processor includes an artificial neural network engine that makes an object recognition model learn by using learning data and weights to provide a learned object recognition model. The memory device, divides a feature vector into a first sub feature vector and a second feature vector, and performs a first calculation to apply the second sub feature vector and the weights to the learned object recognition model to provide a second object recognition result. The artificial neural network engine performs a second calculation to apply the first sub feature vector and the weights to the learned object recognition model to provide a first object recognition result and provides the first object recognition result to the memory device. The second calculation is performed in parallel with the first calculation.

Abstract: A memory system includes a nonvolatile memory electrically connected to a data bus, a DRAM electrically connected to the data bus, and a memory controller configured to drive the DRAM as a cache memory and the nonvolatile memory as a main memory and to synchronize data of a cache line with data of the nonvolatile memory in units of cache units based on a dirty flag. The DRAM is configured to load data of the cache line that caches data stored in the nonvolatile memory and to store the dirty flag, which indicates whether a cache unit is dirty, in units of cache units, where a size of each cache unit is smaller than a size of the cache line.

Abstract: A memory system includes a processor that includes cores and a memory controller, and a first semiconductor memory module that communicates with the memory controller. The cores receive a call to perform a first exception handling in response to detection of a first error when the memory controller reads first data from the first semiconductor memory module. A first monarchy core of the cores performs the first exception handling and the remaining cores of the cores return to remaining operations previously performed.

Abstract: A memory system includes a nonvolatile memory module and a first controller configured to control the nonvolatile memory module. The nonvolatile memory module includes a volatile memory device, a nonvolatile memory device, and a second controller configured to control the volatile memory device and the nonvolatile memory device. The first controller may be configured to transmit a read request to the second controller. When, during a read operation according to the read request, normal data is not received from the nonvolatile memory device, the first controller may perform one or more retransmits of the read request to the second controller without a limitation on a number of times that the first controller performs the one or more retransmits of the read request.

Abstract: A memory system includes a nonvolatile memory electrically connected to a data bus, a DRAM electrically connected to the data bus, and a memory controller configured to drive the DRAM as a cache memory and the nonvolatile memory as a main memory and to synchronize data of a cache line with data of the nonvolatile memory in units of cache units based on a dirty flag. The DRAM is configured to load data of the cache line that caches data stored in the nonvolatile memory and to store the dirty flag, which indicates whether a cache unit is dirty, in units of cache units, where a size of each cache unit is smaller than a size of the cache line.

Abstract: In a memory module including a memory device and a filter, the memory device operates with a clock of a reference frequency. The filter receives a multiplexed signal from a host and filters a signal of a frequency band from the multiplexed signal. The frequency band includes the reference frequency and the signal of the frequency band is provided to the memory device.

Abstract: A memory system includes a nonvolatile memory electrically connected to a data bus, a DRAM electrically connected to the data bus, and a memory controller configured to drive the DRAM as a cache memory and the nonvolatile memory as a main memory and to synchronize data of a cache line with data of the nonvolatile memory in units of cache units based on a dirty flag. The DRAM is configured to load data of the cache line that caches data stored in the nonvolatile memory and to store the dirty flag, which indicates whether a cache unit is dirty, in units of cache units, where a size of each cache unit is smaller than a size of the cache line.

Abstract: In a memory module including a memory device and a filter, the memory device operates with a clock of a reference frequency. The filter receives a multiplexed signal from a host and filters a signal of a frequency band from the multiplexed signal. The frequency band includes the reference frequency and the signal of the frequency band is provided to the memory device.

Abstract: A non-volatile memory device having respective parallel queues is disclosed. The non-volatile memory device includes a plurality of concurrently addressable units. The non-volatile memory device has respective queues for the concurrently addressable units, and transfers a second command to respective queues for the remaining concurrently addressable units while a first command is executed in a part of the concurrently addressable units, and executes a second command in the remaining concurrently addressable units. Accordingly, non-volatile memory device may concurrently access the concurrently addressable units in parallel, and may have high speed.

Abstract: A non-volatile memory device having respective parallel queues is disclosed. The non-volatile memory device includes a plurality of concurrently addressable units. The non-volatile memory device has respective queues for the concurrently addressable units, and transfers a second command to respective queues for the remaining concurrently addressable units while a first command is executed in a part of the concurrently addressable units, and executes a second command in the remaining concurrently addressable units. Accordingly, non-volatile memory device may concurrently access the concurrently addressable units in parallel, and may have high speed.