Western Digital Technologies, Inc.

Sandisk Technologies Inc.

Jaco Hofmann

Dejan Vucinic

Zvonimir Z Bandic

Qingbo Wang

Minghai Qin

Anand Kulkarni

Chao Sun

Marjan Radi

Huynh Tu Dang

A memory device includes a first memory and a second memory that caches data stored in the first memory. At least one controller of the memory device receives page fault information from a host. The page fault information results from a request for data by the host that is stored in the first memory but is not cached in the second memory when requested by the host. The memory device uses the received page fault information for one or more inputs into a prefetch model trained by Machine Learning (ML) to generate at least one inference. Based at least in part on the at least one inference, prefetch data is cached in the second memory. In one aspect, the page fault information is used to train the prefetch model. In another aspect, the page fault information includes at least one virtual address used by the host for the requested data.

Memory prefetch based on machine learning

A system includes at least one memory controller that partitions at least one memory into a plurality of nodes. Blast zones are formed that each include a predetermined number of nodes. Cache lines are erasure encoded to be stored in one or more blast zones with at least two nodes in a blast zone storing respective portions of a cache line and at least one node in the blast zone storing a parity portion. In one aspect, it is determined that data stored in one or more nodes of a blast zone needs to be reconstructed and stored in one or more spare nodes designated to replace the one or more nodes. Erasure decoding is performed using data from one or more other nodes in the blast zone to reconstruct the data for storage in the one or more spare nodes.

Fault tolerance and coherence for shared memory

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Santa Clara, CA, United States of America

Jaco Hofmann