Abstract: 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.

Abstract: 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.

Abstract: Methods and apparatus for processing data in a distributed inference scheme based on sparse inputs. An example method includes receiving an input at a first node. A first sparsified input is generated for a second node based on a set of features associated with the second node, which are identified based on a weight mask having non-zero values for weights associated with features upon which processing by the second node depends and zeroed values for weights associated with other features. The first sparsified input is transmitted to the second node for generating an output of the second node. A second sparsified input is received from the second node and combined into a combined input. The combined input is processed into an output of the first node. The neural network is configured to generate an inference based on processing the outputs of the first node and the second node.

Abstract: 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.