Alibaba Group Holding Limited

Alibaba (china) Co., Ltd.

Alibaba Damo (hangzhou) Technology Co., Ltd.

T-head (shanghai) Semiconductor Co., Ltd.

Tianchan Guan

Hongzhong Zheng

Dimin Niu

Shuangchen Li

Yijin Guan

Heng Liu

Lide Duan

Yuan Gao

Chunsheng Liu

Minghai Qin

Zhibin Xiao

Zhe Zhang

Shengcheng Wang

Honzhong Zheng

Wei Han

A memory module can include a hybrid media controller coupled to a volatile memory, a non-volatile memory, a non-volatile memory buffer and a set of memory mapped input/output (MMIO) register. The hybrid media controller can be configured for reading and writing data to a volatile memory of a memory mapped space of a memory module. The hybrid media controller can also be configured for reading and writing bulk data to a non-volatile memory of the memory mapped space. The hybrid media controller can also be configured for reading and writing data of a random-access granularity to the non-volatile memory of the memory mapped space. The hybrid media controller can also be configured for self-indexed moving data between the non-volatile memory and the volatile memory of the memory module.

Hybrid memory management systems and methods with in-storage processing and attribute data management

This specification describes methods and systems for accessing attribute data in graph neural network (GNN) processing. An example system includes: a plurality of cores, each of the plurality of cores comprises a key-value fetcher and a filter, and is programmable using a software interface to support a plurality of data formats of the GNN attribute data, wherein: the key-value fetcher is programmable using the software interface to perform key-value fetching associated with accessing the GNN attribute data, and the filter of at least one of the plurality of cores is programmable using the software interface to sample node identifiers associated with accessing the GNN attribute data; and a first memory communicatively coupled with the plurality of cores, wherein the first memory is configured to store data shared by the plurality of cores.

Programmable access engine architecture for graph neural network and graph application

Disclosed are systems and methods that determine whether instances of data (e.g., forward activations, backward derivatives of activations) that are used to train deep neural networks are to be stored on-chip or off-chip. The disclosed systems and methods are also used to prune the data (discard or delete selected instances of data). A system includes a hierarchical arrangement of on-chip and off-chip memories, and also includes a hierarchical arrangement of data selector devices that are used to decide whether to discard data and where in the system the data is to be discarded.

Hierarchical methods and systems for storing data

The present application discloses a computing system for implementing an artificial neural network model. The artificial neural network model has a structure of multiple layers. The computing system comprises a first processing unit, a second processing unit, and a third processing unit. The first processing unit performs computations of the first layer based on a first part of input data of the first layer to generate a first part of output data. The second processing unit performs computations of the first layer based on a second part of the input data of the first layer so as to generate a second part of the output data. The third processing unit performs computations of the second layer based on the first part and the second part of the output data. The first processing unit, the second processing unit, and the third processing unit have the same structure.

Computing system for implementing artificial neural network models and method for implementing artificial neural network models

The present application provides a computer system, a memory expansion device and a method for use in the computer system. The computer system includes multiple hosts and multiple memory expansion devices; the memory expansion devices correspond to the hosts in a one-to-one manner. Each host includes a CPU and a memory; each memory expansion device includes a first interface and multiple second interfaces. The first interface is configured to allow each memory expansion device to communicate with the corresponding CPU via a first coherence interconnection protocol, and the second interface is configured to allow each memory expansion device to communicate with a portion of memory expansion devices via a second coherence interconnection protocol. Any two memory expansion devices communicate with each other via at least two different paths, and the number of memory expansion devices that at least one of the two paths passes through is not more than one.

Computer system, memory expansion device and method for use in computer system

A system-in-package including a logic die and one or more memory dice can include a reliability availability serviceability (RAS) memory management unit (MMU) for memory error detection, memory error prediction and memory error handling. The RAS MMU can receive memory health information, on-die memory error information, system error information and read address information for the one or more memory dice. The RAS MMU can manage the memory blocks of the one or more memory dice based on the memory health information, on-die memory error type, system error type and read address. The RAS MMU can also further manage the memory blocks based on received on-die memory temperature information and or system temperature information.

Error detection, prediction and handling techniques for system-in-package memory architectures

A memory management method of a data processing system is provided. The memory management method includes: creating a first memory zone and a second memory zone related to a first node of a first server, wherein the first server is located in the data processing system, and the first node includes a processor and a first memory; mapping the first memory zone to the first memory; and mapping the second memory zone to a second memory of a second server, wherein the second server is located in the data processing system, and the processor is configured to access the second memory of the second server through an interface circuit of the first server and through an interface circuit of the second server.

Data processing system and memory management method of data processing system

Apparatus, method, and system provided herein are directed to prioritizing cache line writing of compressed data. The memory controller comprises a cache line compression engine that receives raw data, compresses the raw data, determines a compression rate between the raw data and the compressed data, determines whether the compression rate is greater than a predetermined rate, and outputs the compressed data as data-to-be-written if the compression rate is greater than the predetermined rate. In response to determining that the compression rate is greater than the predetermined rate, the cache line compression engine generates a compression signal indicating the data-to-be-written is the compressed data and sends the compression signal to a scheduler of a command queue in the memory controller where writing of compressed data is prioritized.

Memory controller prioritizing writing compressed data

An embodiment of the present disclosure relates to a graph node sampling system and a computer-implemented method, where structure information of nodes in a graph neural network is stored in a set of data structures, and attribute data of the nodes is stored in another set of data structures. Node sampling may be performed by a sampling unit in a solid state drive. A node sampling unit selects, reads, and collects attribute data of a sampled node and a neighboring node of the sampled node, and transfers the data to a main memory. The method and system according to the embodiments of the present disclosure can save bandwidth consumed by node sampling in large applications such as a graph neural network.

System for graph node sampling and method implemented by computer

The present invention provides systems and methods for efficiently and effectively priming and initializing a memory. In one embodiment, a memory controller includes a normal data path and a priming path. The normal data path directs storage operations during a normal memory read/write operation after power startup of a memory chip. The priming path includes a priming module, wherein the priming module directs memory priming operations during a power startup of the memory chip, including forwarding a priming pattern for storage in a write pattern mode register of a memory chip and selection of a memory address in the memory chip for initialization with the priming pattern. The priming pattern includes information corresponding to proper initial data values. The priming pattern can also include proper corresponding error correction code (ECC) values. The priming module can include a priming pattern register that stores the priming pattern.

Memory priming and initialization systems and methods

The present application discloses a computing system and an associated method. The computing system includes a first host, a second host, a first memory extension device and a second memory extension device. The first host includes a first memory, and the second host includes a second memory. The first host has a plurality of first memory addresses corresponding to a plurality of memory spaces of the first memory, and a plurality of second memory addresses corresponding to a plurality of memory spaces of the second memory. The first memory extension device is coupled to the first host. The second memory extension device is coupled to the second host and the first memory extension device. The first host accesses the plurality of memory spaces of the second memory through the first memory extension device and the second memory extension device.

Computing system and associated method

A data processing system includes a first server and a second server. The first server includes a first processor group, a first memory space and a first interface circuit. The second server includes a second processor group, a second memory space and a second interface circuit. The first memory space and the second memory space are allocated to the first processor group. The first processor group is configured to perform memory error detection to generate an error log corresponding to a memory error. When the memory error occurs in the second memory space, the first interface circuit is configured to send the error log to the second interface circuit, and the second processor group is configured to log the memory error according to the error log received by the second interface circuit. The data processing system is capable of realizing memory reliability architecture supporting operations across different servers.

Data processing system

This specification describes methods and systems for accelerating attribute data access for graph neural network (GNN) processing. An example method includes: receiving a root node identifier corresponding to a node in a graph for GNN processing; determining one or more candidate node identifiers according to the root node identifier, wherein attribute data corresponding to the one or more candidate node identifiers are sequentially stored in a memory; and sampling one or more graph node identifiers at least from the one or more candidate node identifiers for the GNN processing.

Access friendly memory architecture of graph neural network sampling

This application describes a hardware accelerator, a computer system, and a method for accelerating Graph Neural Network (GNN) node attribute fetching. The hardware accelerator comprises a GNN attribute processor; and a first memory, wherein the GNN attribute processor is configured to: receive a graph node identifier; determine a target memory address within the first memory based on the graph node identifier; determine, based on the received graph node identifier, whether attribute data corresponding to the received graph node identifier is cached in the first memory at the target memory address; and in response to determining that the attribute data is not cached in the first memory: fetch the attribute data from a second memory, and write the fetched attribute data into the first memory at the target memory address.

Graph neural network accelerator with attribute caching

A programmable device receives commands from a processor and, based on the commands: identifies a root node in a graph; identifies nodes in the graph that are neighbors of the root node; identifies nodes in the graph that are neighbors of the neighbors; retrieves data associated with the root node; retrieves data associated with at least a subset of the nodes that are neighbors of the root node and that are neighbors of the neighbor nodes; and writes the data that is retrieved into a memory.

Growing community of inventors

Shanghai, China

Tianchan Guan