Samsung Electronics Co., Ltd.

Alibaba Group Holding Limited

Alibaba (China) Co., Ltd.

Alibaba Damo (Hangzhou) Technology Co., Ltd.

Alibaba Innovation Private Limited

Dimin Niu

Hongzhong Zheng

Mu-Tien Chang

Shuangchen Li

Krishna Teja Malladi

Jangseok Choi

Sun Young Lim

Indong Kim

Tianchan Guan

Hyun-Joong Kim

Won-hyung Song

Yijin Guan

Lide Duan

Wei Han

Yuhao Wang

Craig Hanson

Chaohong Hu

Dongyan Jiang

Peng Gu

Fei Xue

Sun-Young Lim

Shengcheng Wang

Bob Brennan

Xiaoxin Fan

Jilan Lin

Frederic Sala

Prasun Gera

Shaungchen Li

Youngjin Cho

Fei Sun

Jae-Gon Lee

Yuan Xie

Heehyun Nam

Youngsik Kim

Heng Liu

Zhe Zhang

Kyung-Chang Ryoo

Weifeng Zhang

Yuanwei Fang

Yawen Zhang

Chunsheng Liu

Guoyang Chen

In-Su Choi

Suhas

Zhibin Xiao

Hongyu Liu

Mu Tien Chang

Jiacheng Ma

Yingmin Li

Honzhong Zheng

Shuangcheng Li

In-Dong Kim

Xipeng Shen

Jingjun Chu

A physical host includes a memory, a first buffer, a second buffer, a third buffer and a processor. The first buffer stores a log regarding dirty pages. The second buffer stores a dirty bitmap, where the dirty bitmap is written into the second buffer according to the log read from the first buffer. The third buffer stores the dirty bitmap. The processor obtains the current memory address to be migrated and a destination memory address, and marks a page table corresponding to the memory address to be migrated as dirty pages and writes the log marked as the dirty pages into the first buffer when the memory address to be migrated is written. The processor includes a memory copy engine for reading the dirty bitmap from the third buffer, and copying the content corresponding to the dirty pages to the destination memory according to the dirty bitmap.

Physical host optimizing memory migration, operation method of physical host, and computer-readable storage medium thereof

A configurable processing unit including a core processing element and a plurality of assist processing elements can be coupled together by one or more networks. The core processing element can include a large processing logic, large non-volatile memory, input/output interfaces and multiple memory channels. The plurality of assist processing elements can each include smaller processing logic, smaller non-volatile memory and multiple memory channels. One or more bitstreams can be utilized to configure and reconfigure computation resources of the core processing element and memory management of the plurality of assist processing elements.

Configurable processing architecture

This application describes systems and methods for facilitating memory access for graph neural network (GNN) processing. An example system includes a plurality of processing units, each configured to perform graph neural network (GNN) processing; and a plurality of memory extension cards, each configured to store graph data for the GNN processing, wherein: each of the plurality of processing units is communicatively coupled with three other processing units via one or more interconnects respectively; the plurality of processing units are communicatively coupled with the plurality of memory extension cards respectively; and each of the plurality of memory extension cards includes a graphic access engine circuitry configured to acceleratre GNN memory access.

Smart memory extension to processors

A memory module includes a memory array, an interface and a controller. The memory array includes an array of memory cells and is configured as a dual in-line memory module (DIMM). The DIMM includes a plurality of connections that have been repurposed from a standard DIMM pin out configuration to interface operational status of the memory device to a host device. The interface is coupled to the memory array and the plurality of connections of the DIMM to interface the memory array to the host device. The controller is coupled to the memory array and the interface and controls at least one of a refresh operation of the memory array, control an error-correction operation of the memory array, control a memory scrubbing operation of the memory array, and control a wear-level control operation of the array, and the controller to interface with the host device.

Coordinated in-module RAS features for synchronous DDR compatible memory

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

A high-bandwidth memory (HBM) includes a memory and a controller. The controller receives a data write request from a processor external to the HBM and the controller stores an entry in the memory indicating at least one address of data of the data write request and generates an indication that a data bus is available for an operation during a cycle time of the data write request based on the data write request comprising sparse data or data-value similarity. Sparse data includes a predetermined percentage of data values equal to zero, and data-value similarity includes a predetermined amount of spatial value locality of the data values. The predetermined percentage of data values equal to zero of sparse data and the predetermined amount of spatial value locality of the special-value pattern are both based on a predetermined data granularity.

High bandwidth memory system

The present disclosure discloses a processor. The processor is used to perform parallel computation and includes a logic die and a memory die. The logic die includes a plurality of processor cores and a plurality of networks on chip, wherein each processor core is programmable. The plurality of networks on chip are correspondingly connected to the plurality of processor cores, so that the plurality of processor cores form a two-dimensional mesh network. The memory die and the processor core are stacked vertically, wherein the memory die includes a plurality of memory tiles, and when the processor performs the parallel computation, the plurality of memory tiles do not have cache coherency; wherein, the plurality of memory tiles correspond to the plurality of processor cores in a one-to-one or one-to-many manner.

Two-dimensional processing array with a vertically stacked memory tile array

A processor includes a plurality of memory units, each of the memory units including a plurality of memory cells, wherein each of the memory units is configurable to operate as memory, as a computation unit, or as a hybrid memory-computation unit.

Scaling out architecture for dram-based processing unit (DPU)

This application describes systems and methods for facilitating memory access for graph neural network (GNN) processing. An example method includes fetching, by an access engine circuitry implemented on a circuitry board, a portion of structure data of a graph from a pinned memory in a host memory of a host via a first peripheral component interconnect express (PCIe) connection; performing node sampling using the fetched portion of the structure data of the graph to select one or more sampled nodes; fetching, by the access engine circuitry, a portion of attribute data of the graph from the pinned memory via the first PCIe connection; sending the fetched portion of the attribute data of the graph to one or more processors; and performing, by the one or more processors, GNN processing for the graph using the fetched portion of the attribute data of the graph.

Graph acceleration solution with cloud FPGA

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

A high bandwidth memory system. In some embodiments, the system includes: a memory stack having a plurality of memory dies and eight 128-bit channels; and a logic die, the memory dies being stacked on, and connected to, the logic die; wherein the logic die may be configured to operate a first channel of the 128-bit channels in: a first mode, in which a first 64 bits operate in pseudo-channel mode, and a second 64 bits operate as two 32-bit fine-grain channels, or a second mode, in which the first 64 bits operate as two 32-bit fine-grain channels, and the second 64 bits operate as two 32-bit fine-grain channels.

Bandwidth boosted stacked memory

A method of correcting a memory error of a dynamic random-access memory module (DRAM) using a double data rate (DDR) interface, the method includes conducting a memory transaction including multiple bursts with a memory controller to send data from data chips of the DRAM to the memory controller, detecting one or more errors using an ECC chip of the DRAM, determining a number of the bursts having the errors using the ECC chip of the DRAM, determining whether the number of the bursts having the errors is greater than a threshold number, determining a type of the errors, and directing the memory controller based on the determined type of the errors, wherein the DRAM includes a single ECC chip per memory channel.

DRAM assist error correction mechanism for DDR SDRAM interface

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

The presented systems enable efficient and effective network communications. The presented systems enable efficient and effective network communications. In one embodiment a memory device includes a memory module, including a plurality of memory chips configured to store information; and an inter-chip network (ICN)/shared smart memory extension (SMX) memory interface controller (ICN/SMX memory interface controller) configured to interface between the memory module and an inter-chip network (ICN), wherein the ICN is configured to communicatively couple the memory device to a parallel processing unit (PPU). In one exemplary implementation, the ICN/SMX memory controller includes a plurality of package buffers, an ICN physical layer interface, a PRC/MAC interface, and a switch. The memory device and be a memory card including memory module (e.g., DDR DIMM, etc.).

Flexible memory extension systems and methods

Growing community of inventors

Sunnyvale, CA, United States of America

Dimin Niu