Intel Corporation

Sourabh Dongaonkar

Jawad Basit Khan

Chetan Chauhan

Mariano Tepper

Dipanjan Sengupta

Richard L Coulson

Theodore L Willke

Rajesh Sundaram

Sandeep K Guliani

Wei Wu

William K Waller

A memory accessed by rows and/or by columns in which an array of bits can be physically stored physical one-bit wide columns with each bit of the multi-bit wide logical column stored in a one-bit physical column in a different physical die. The multi-bit column is read by reading a one-bit physical column in each of the different physical die in parallel. The multi-bit wide logical column is arranged diagonally across M physical rows and M one-bit physical columns with each bit of the multi-bit wide logical column in the logical row stored in a different physical row and physical one-bit wide column in one of plurality of dies.

Method and apparatus to perform a multiple bit column read using a single bit per column memory accessible by row and/or by column

Examples described herein relate to an apparatus comprising: circuitry to receive a request to store data as a part of a matrix in a memory device; circuitry to allocate address mappings to the data to reduce a number of sequential accesses to a same partition of a portion of the memory device; circuitry to store the address mappings for access with a read operation; and circuitry to cause storage of the data into the memory device according to the address mappings. In some examples, the matrix comprises one or more columns and/or one or more rows. In some examples, the memory device comprises one or more of: a three-dimensional (3D) cross point memory device, volatile memory device, or non-volatile memory device.

Data access ordering for writing-to or reading-from memory devices

A memory accessed by rows and/or by columns in which an array of bits can be physically stored in multi-bit wide columns in physically contiguous rows is provided. A multi-bit wide logical column is arranged diagonally across (M/multi-bits) physical rows and (M/multi-bits) physical columns with each of the plurality of multi-bit wide logical columns in the logical row stored in a different physical row and physical multi-bit column.

Method and apparatus to perform a read of a column in a memory accessible by row and/or by column

Technologies for performing random sparse lifting and Procrustean orthogonal sparse hashing using column read-enabled memory include a device that has a memory that is column addressable and circuitry connected to the memory. The circuitry is configured to add a set of input data vectors to the memory as a set of binary dimensionally expanded vectors, including multiplying each input data vector with a projection matrix. The circuitry is also configured to produce a search hash code from a search data vector, including multiplying the search data vector with the projection matrix. Further, the circuitry is configured to determine a Hamming distance between the search hash code and each of the binary dimensionally expanded vectors.

Technologies for performing sparse lifting and procrustean orthogonal sparse hashing using column read-enabled memory

Technologies for refining stochastic similarity search candidates include a device having a memory that is column addressable and circuitry connected to the memory. The circuitry is configured to add a set of input data vectors to the memory as a set of binary dimensionally expanded vectors, including multiplying each input data vector with a projection matrix. The circuitry is also configured to produce a search hash code from a search data vector, including multiplying the search data vector with the projection matrix. Additionally, the circuitry is configured to identify a result set of the binary dimensionally expanded vectors as a function of a Hamming distance of each binary dimensionally expanded vector from the search hash code and determine, from the result set, a refined result set as a function of a similarity measure in an original input space of the input data vectors.

Technologies for refining stochastic similarity search candidates

Technologies for performing stochastic similarity searches in an online clustering space include a device having a column addressable memory and circuitry. The circuitry is configured to determine a Hamming distance from a binary dimensionally expanded vector to each cluster of a set of clusters of binary dimensionally expanded vectors in the memory, identify the cluster having the smallest Hamming distance from the binary dimensionally expanded vector, determine whether the identified cluster satisfies a target size, and add or delete, in response to a determination that the identified cluster does not satisfy the target size, the binary dimensionally expanded vector to or from the identified cluster.

Technologies for performing stochastic similarity searches in an online clustering space

Technologies for tuning performance and/or accuracy of similarity search using stochastic associative memories (SAM). Under a first subsampling approach, columns associated with set bits in a search key comprising a binary bit vector are subsampled. Matching set bits for the subsampled columns are aggregated on a row-wise basis to generate similarity scores, which are then ranked. A similar scheme is applied for all the columns with set bits in the search key and the results for top ranked rows are compared to evaluate a tradeoff between throughput boost versus lost accuracy. A second approach called continuous column read, and iterative approach is employed that continuously scores the rows as each new column read is complete. The similarity scores for an N-1 and Nth-1 iteration are ranked, a rank correlation is calculated, and a determination is made to whether the rank correlation meets or exceeds a threshold.

Technologies for tuning performance and/or accuracy of similarity search using stochastic associative memories

Technologies for column reads for clustered data include a device having a column-addressable memory and circuitry connected to the memory. The column-addressable memory includes multiple dies. The circuitry may be configured to determine multiple die offsets based on a logical column number of the data cluster, determine a base address based on the logical column number, program the dies with the die offsets. The circuitry is further to read logical column data from the column-addressable memory. To read the data, each die adds the corresponding die offset to the base address. The column-addressable memory may include multiple command/address buses. The circuitry may determine a starting address for each of multiple logical columns and issue a column read for each starting address via a corresponding command/address bus. Other embodiments are described and claimed.

Technologies for performant column read operations on clustered data in a dimm architecture

Technologies for media management for providing column data layouts for clustered data include a device having a column-addressable memory and circuitry connected to the memory. The circuitry is configured to store a data cluster of a logical matrix in the column-addressable memory with a column-based format and to read a logical column of the data cluster from the column-addressable memory with a column read operation. Reading the logical column may include reading logical column data diagonally from the column-address memory, including reading from the data cluster and a duplicate copy of the data cluster. Reading the logical column may include reading from multiple complementary logical columns. Reading the logical column may include reading logical column data diagonally with a modulo counter. The column data may bread from a partition of the column-address memory selected based on the logical column number. Other embodiments are described and claimed.

Technologies for column-based data layouts for clustered data systems

Technologies for performing encoding of data symbols for column read operations include a device having a memory that is column addressable and circuitry connected to the memory. The circuitry is configured to obtain a data set to encode. The data set is defined by a set of data symbols. The circuitry is also configured to determine a set of codewords to encode the data symbols of the data set, including defining each codeword with a set bit distance of at least two from every other codeword in the set of codewords. Additionally, the circuitry is configured to write the data set to the memory as a function of the determined set of codewords.

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Santa Clara, CA, United States of America

Sourabh Dongaonkar