Memory expander, computing systems, and operating method of the host device

1 . A memory expander comprising:

a memory;

a memory controller configured to control the memory;

a compute express link (CXL) engine configured to acquire a CXL flit associated with a calculation request for pieces of data stored in the memory by accessing a host memory of a host device connected to the memory expander and configured to acquire a command and data associated with the calculation request by performing conversion on the CXL flit; and

a domain-specific accelerator configured to perform a calculation between tensors corresponding to the calculation request based on the command and the data.

2 . The memory expander of claim 1 , wherein the memory expander is a type-3 device defined in a CXL protocol.

3 . The memory expander of claim 1 , further comprising:

an interface register configured to acquire a doorbell signal from the host device,

wherein the doorbell signal is a signal indicating that the command and the data associated with the calculation request are written to a host memory of the host device.

4 . The memory expander of claim 1 , wherein the domain-specific accelerator comprises:

a request queue configured to store the calculation request of the host device;

a scheduler configured to allocate the calculation request to at least one tensor calculation accelerator;

the at least one tensor calculation accelerator configured to perform a calculation between the tensors in response to the allocated calculation request;

a tensor reading module configured to read the tensors from the memory; and

a multiplexer configured to support the at least one tensor calculation accelerator to share the tensor reading module.

5 . The memory expander of claim 4 , wherein the at least one tensor calculation accelerator comprises:

at least one element-wise calculation module configured to perform a first operation on corresponding elements among elements forming each of the tensors; and

a tensor reduction module configured to perform a second operation on a result of the first operation.

6 . The memory expander of claim 4 , wherein

each of the tensors is divided and stored in a plurality of memory expanders comprising the memory expander, and

the at least one tensor calculation accelerator is configured to perform a calculation on at least a portion of the tensors.

7 . The memory expander of claim 4 , wherein

each of the tensors corresponds to an embedding vector, and

the at least one tensor calculation accelerator is configured to calculate a similarity between an input vector acquired from the host device and stored in the memory and a vector pre-stored in the memory.

8 . The memory expander of claim 7 , wherein the similarity corresponds to a Euclidean distance between the input vector and the vector or an angular distance between the input vector and the vector.

9 . A computing system comprising:

a host device configured to store a command and data associated with a calculation request in a host memory and configured to transmit, to a memory expander, a doorbell signal indicating that the command and the data associated with the calculation request are written to the host memory, and

the memory expander configured to perform a calculation between tensors corresponding to the calculation request in response to receiving the doorbell signal and configured to transmit a result of the calculation between the tensors to the host memory,

wherein the tensors are stored in the memory expander.

10 . The computing system of claim 9 , wherein the doorbell signal is received by an interface register of the memory expander.

11 . The computing system of claim 9 , wherein the memory expander is configured to:

acquire a compute express link (CXL) flit associated with the calculation request by accessing the host memory;

acquire the command and the data by performing conversion on the CXL flit; and

perform a calculation between the tensors based on the command and the data.

12 . The computing system of claim 11 , wherein the accessing of the memory expander to the host memory is direct memory access (DMA).

13 . The computing system of claim 11 , wherein the calculation between the tensors is performed by a domain-specific accelerator comprised in the memory expander.

14 . The computing system of claim 9 , wherein the memory expander is a type-3 device defined in a CXL protocol.

15 . An operating method of a host device configured to perform an approximate nearest neighbor search based on a search node, the operating method comprising:

acquiring graph data, wherein each of nodes of the graph data corresponds to tensors stored in a memory expander;

acquiring information of a neighbor node neighboring the search node by searching for the graph data;

acquiring a result of a calculation between the tensors stored in the memory expander and an input tensor input to the host device, based on the information of the neighbor node; and

updating a candidate array based on the result of the calculation,

wherein the candidate array comprises, among the tensors stored in the memory expander, information about a tensor that is similar to the input tensor.

16 . The operating method of claim 15 , wherein the calculation is performed by the memory expander, which is a type-3 device defined in a compute express link (CXL) protocol.

17 . The operating method of claim 15 , wherein the candidate array comprises the nodes comprised in the graph data,

wherein the nodes are mapping of a correspondence relationship between the nodes and the tensors stored in the memory expander, whether the nodes visit, and a calculation result between tensors corresponding to the nodes and the input tensor.

18 . The operating method of claim 15 , wherein the updating of the candidate array comprises:

inserting information into the candidate array;

sorting nodes of the candidate array into which the information is inserted; and

selecting a subsequent search node from the sorted nodes of the candidate array.

19 . The operating method of claim 18 , wherein the inserting of the information into the candidate array comprises:

displaying whether the search node visits;

adding the neighbor node to the candidate array; and

mapping the result of the calculation to the neighbor node.

20 . The operating method of claim 18 , wherein the selecting of the subsequent search node further comprises selecting, from among unvisited nodes in the sorted nodes of the candidate array, a node having a highest similarity with the input tensor as the subsequent search node.