Abstract: A data storage device includes a memory array including a data region and a journal region; and a control circuit configured to generate journal region information including an address of the journal region, process a transaction command corresponding to a transaction based on the journal region information, and perform a checkpointing operation on a selected transaction among a predetermined number of transactions.

Abstract: Provided is an apparatus for accelerating graph neural network (GNN) pre-processing, the apparatus including a set-partitioning accelerator configured to sort each edge of an original graph stored in a coordinate list (COO) format by a node number, perform radix sorting based on a vertex identification (VID) to generate a COO array of a preset length, and perform uniform random sampling on some nodes of a given node array, a merger configured to merge the COO array of the preset length to generate one sorted COO array, a re-indexer configured to assign new consecutive VIDs respectively to the nodes selected through the uniform random sampling, and a compressed sparse row (CSR) converter configured to the edges sorted by the node number into a CSR format.

Abstract: A method and electronic circuit for memory replacement are provided. The method for memory replacement includes generating an input signal in response to an event for a memory, providing the input signal to a time-varying circuit including a plurality of time-varying devices, generating an output signal by reading a value stored in at least one time-varying device among the plurality of time-varying devices, and determining a storage space for replacement, based on the output signal.

Abstract: A computational storage supporting graph machine learning acceleration includes a solid state drive (SSD) configured to store a graph data set; and a field-programmable gate array (FPGA) configured to download, to a memory, a graph machine learning model programmed in a form of a data flow graph by a host, wherein a hardware logic built in the FPGA performs access to the SSD through a peripheral component interconnect-express (PCIe) switch.

Abstract: Provided is an apparatus for accelerating graph neural network (GNN) pre-processing, the apparatus including a set-partitioning accelerator configured to sort each edge of an original graph stored in a coordinate list (COO) format by a node number, perform radix sorting based on a vertex identification (VID) to generate a COO array of a preset length, and perform uniform random sampling on some nodes of a given node array, a merger configured to merge the COO array of the preset length to generate one sorted COO array, a re-indexer configured to assign new consecutive VIDs respectively to the nodes selected through the uniform random sampling, and a compressed sparse row (CSR) converter configured to the edges sorted by the node number into a CSR format.

Abstract: Provided is an apparatus for accelerating graph neural network (GNN) pre-processing, the apparatus including a conversion unit configured to convert an original graph in a coordinate list (COO) format into a graph in a compressed sparse row (CSR) format, a sub-graph generation unit configured to generate a sub-graph with a reduced degree of the graph in the CSR format, and an embedding table generation unit configured to generate an embedding table corresponding to the sub-graph.

Abstract: A compute express link (CXL) computing system includes a host device including a CPU that supports CXL, and a CXL storage connected to a CXL root port of the CPU based on the CXL interconnect and including a flash memory-based memory module.

Abstract: Disclosed is a memory disaggregation computing system including a host server and a memory device connected through a compute express link (CXL) network, in which a computing complex of the host server is connected to a memory resource of the memory device through a CXL packet transmitted through the CXL network, and executes an application program by using the memory resource.

Abstract: Provided is an apparatus for accelerating a graph neural network for efficient parallel processing of massive graph datasets, including a streaming multiprocess (SM) scheduler and a computation unit, wherein the SM scheduler obtains a subgraph and an embedding table per layer, determines a number of SMs to be allocated for processing embeddings of a destination-vertex based on a feature dimension and a maximum number of threads in each of the SMs, and allocates the determined number of SMs to each of all destination-vertices included in the subgraph, and the computation unit obtains, by each of the SMs, embeddings of a destination-vertex allocated to each SM, obtains, by each SM, embeddings of at least one or more neighbor-vertices of the destination-vertex using the subgraph, and performs, by each SM, a user-designated operation using the embeddings of the destination-vertex and the embeddings of the neighbor-vertices.

Abstract: A computational storage supporting graph machine learning acceleration includes a solid state drive (SSD) configured to store a graph data set; and a field-programmable gate array (FPGA) configured to download, to a memory, a graph machine learning model programmed in a form of a data flow graph by a host, wherein a hardware logic built in the FPGA performs access to the SSD through a peripheral component interconnect-express (PCIe) switch.

Abstract: A first IP address is set to the host, and a second IP address is set to the storage card. A storage card includes a storage device and a processor for executing firmware. The host converts a first Ethernet packet including an ISP-related request and destined for the second IP address into an NVMe request according to an NVMe protocol, and transfers the NVMe request to the storage card. The firmware parses the NVMe request to perform the ISP-related request.

Abstract: A first IP address is set to the host, and a second IP address is set to the storage card. A storage card includes a storage device and a processor for executing firmware. The host converts a first Ethernet packet including an ISP-related request and destined for the second IP address into an NVMe request according to an NVMe protocol, and transfers the NVMe request to the storage card. The firmware parses the NVMe request to perform the ISP-related request.

Abstract: A memory controlling device of a computing device including a CPU, a memory, and a flash-based storage device is provided. The memory controlling device includes an address manager and an interface. The address manager aggregates a memory space of the memory and a storage space of the storage device into an expanded memory space, and handles a memory request for the expanded memory space from the CPU by using the memory space of the memory as a cache for the storage space of the storage device. The interface is used to access the memory and the storage device.

Abstract: A memory controlling device of a computing device including a CPU, a memory, and a flash-based storage device is provided. The memory controlling device includes an address manager and an interface. The address manager aggregates a memory space of the memory and a storage space of the storage device into an expanded memory space, and handles a memory request for the expanded memory space from the CPU by using the memory space of the memory as a cache for the storage space of the storage device. The interface is used to access the memory and the storage device.