Search acceleration for artificial intelligence

Patent metadata

Field	Value
Publication number	US-11966841-B2
Application number	US-202117159982-A
Country	US
Kind code	B2
Filing date	Jan 27, 2021
Priority date	Jan 31, 2018
Publication date	Apr 23, 2024
Grant date	Apr 23, 2024

Abstract

An apparatus for artificial intelligence acceleration is provided. The apparatus includes a storage and compute system having a distributed, redundant key value store for metadata. The storage and compute system having distributed compute resources configurable to access, through a plurality of authorities, data in the solid-state memory, run inference with a deep learning model, generate vectors for the data and store the vectors in the key value store.

First claim

What is claimed is:

1. An apparatus, comprising:
a storage and compute system having a distributed, redundant key value store for metadata; and
the storage and compute system having distributed compute resources configurable to store metadata for one or more buckets containing data and create views containing links to the data contained in the one or more buckets and stored in memory, the data associated with a similarity predicate based on vectors in the key value store.

2. The apparatus of claim 1 , further comprising:
the storage and compute system having at least one network port, and configurable to receive an object or file, run the inference for the object or the file with the deep learning model, based on the vectors in the key value store, and output a response based on the inference.

3. The apparatus of claim 1 , wherein storage capacity of the memory is sufficient to hold all of the data for the inference with the deep learning model throughout the processing of an entire data pipeline.

4. The apparatus of claim 1 , wherein the memory is solid state memory.

5. The apparatus of claim 1 , further comprising:
the distributed compute resources configurable to access data in the memory, run inference with a deep learning model, generate vectors for the data and store the vectors in the key value store.

6. The apparatus of claim 1 , further comprising:
the distributed compute resources configurable to compute a vector of an object for a search, perform a hash operation on the vector, search for vectors in the key value store that are within a specified distance of the vector, based on a result of the hash operation, and retrieve one or more full vectors for objects stored in the memory based on a result of the search, wherein the vectors are stored in the key value store using a locality-preserving hash operation.

7. The apparatus of claim 1 , further comprising:
the distributed compute resources configurable to receive tags associated with the data, and generate a list or count of data entities having tags that satisfy a predicate.

8. A method, comprising:
generating vectors for data based on performing inference with a deep learning model:
storing the vectors in a key value store;
storing metadata for one or more buckets containing examples associated with unstructured data; and
generating views containing links to the examples that satisfy a similarity predicate, based on a search of the vectors.

9. The method of claim 8 , further comprising:
performing the inference for an object with the deep learning model based on vectors in the key value store.

10. The method of claim 8 , further comprising:
outputting a response based on the inference.

11. The method of claim 8 , wherein the system is configurable to write and receive unstructured data into solid state memory of the system.

12. The method of claim 8 , wherein the key value store is a distributed key value store, across storage nodes of the system.

13. The apparatus of claim 9 , further comprising:
search for vectors in the key value store that are within a specified distance of a computed vector associated with a search.

14. The method of claim 8 , wherein the vectors are stored in the key value store using a locality-preserving hash operation.

15. A tangible, non-transitory, computer-readable media having instructions thereupon which, when executed by a processor, cause the processor to perform a method comprising:
generating vectors for data based on performing inference with a deep learning model:
storing the vectors in a key value store;
storing metadata for one or more buckets containing examples associated with unstructured data; and
generating views containing links to the examples that satisfy a similarity predicate, based on a search of the vectors.

16. The computer readable media of claim 15 , further comprising:
performing the inference for an object with the deep learning model based on vectors in the key value store.

17. The computer readable media of claim 15 , wherein the method further comprises:
outputting a response based on the inference.

18. The computer readable media of claim 15 , wherein the system is configurable to write and receive unstructured data into solid state memory of the system.

19. The computer readable media of claim 15 , wherein the key value store is a distributed key value store, across storage nodes of the system.

20. The computer readable media of claim 15 , wherein the vectors are stored in the key value store using a locality-preserving hash operation.

Assignees

• Pure Storage Inc

Inventors

• Margaglia Fabio
• Potyraj Emily
• Kannan Hari
• Sandvig Cary A

Classifications

• G06N3/08 Primary

  Learning methods · CPC title

• G06F16/30

  of unstructured textual data (document management systems G06F16/93) · CPC title

• G06N5/04

  Inference or reasoning models · CPC title

• G06F3/061 Primary

  Improving I/O performance · CPC title

• G06N3/063

  using electronic means · CPC title