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US 10,120,648 B2Granted
Method of improving search quality by combining high precision and low precision computing
AssigneeSingular Computing LLC
InventorJoseph Bates
Life of this patent
Estimating the term…
Abstract
Low precision computers can be efficient at finding possible answers to search problems. However, sometimes the task demands finding better answers than a single low precision search. A computer system augments low precision computing with a small amount of high precision computing, to improve search quality with little additional computing.
Claims8
A device comprising:
- a plurality of low precision high dynamic range (LPHDR) execution units;
- at least one high precision execution unit, wherein the precision of computations performed by the at least one high precision execution unit is higher than the precision of computations performed by the plurality of LPHDR execution units;
- means for controlling the plurality of LPHDR execution units and the at least one high precision execution unit to perform a method, the method comprising:
- (A) receiving, as input, a plurality of example vectors and a test vector;
- (B) storing the plurality of example vectors;
- (C) using the plurality of LPHDR execution units to:
- (C) (1) identifying a first example vector, within the plurality of example vectors, that is nearest to the test vector;
- (C) (2) storing an identifier of the first example vector;
- (C) (3) identifying a second example vector, within the plurality of example vectors, that is second nearest to the test vector;
- and
- (C) (4) storing an identifier of the second example vector;
- (D) using the at least one high precision execution unit to:
- (D) (1) compute a first distance between the test vector and the first example vector;
- (D) (2) compute a second distance between the test vector and the second example vector;
- (D) (3) identify the lessor of the first distance and the second distance;
- (D) (4) identify, from among the first example vector and the second example vector, an example vector associated with the lessor of the first distance and the second distance;
- and
- (D) (5) output the example vector associated with the lessor of the first distance and the second distance.
15 more elements
Dependencies are inferred from the claim text.