| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_fffda8b59ac378c0e7cd94cdf51bbfd7 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G11C2213-77 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H04L49-101
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G11C13-0007
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-02
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H03K19-17748
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H04Q3-0004
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F17-16
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G11C13-0069
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F9-30036
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/H04Q11-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-063 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/H04Q3-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/H04Q11-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F9-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/H04L12-933
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/H03K19-17748
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G11C13-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-063
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F17-16
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-02 |
| filingDate |
2019-10-30-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate |
2020-10-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5f07473df1b8d1a0696b47692e46d29e
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_dfba2d88d5ca8e3f0ad8d1cc0924c1a6
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b7e84c61c84403c9d603c46871eb6aec
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ff7e7868953a2693634f76d1bd3a58a2 |
| publicationDate |
2020-10-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-10812083-B2 |
| titleOfInvention |
Techniques for computing dot products with memory devices |
| abstract |
Sparse representation of information performs powerful feature extraction on high-dimensional data and is of interest for applications in signal processing, machine vision, object recognition, and neurobiology. Sparse coding is a mechanism by which biological neural systems can efficiently process complex sensory data while consuming very little power. Sparse coding algorithms in a bio-inspired approach can be implemented in a crossbar array of memristors (resistive memory devices). This network enables efficient implementation of pattern matching and lateral neuron inhibition, allowing input data to be sparsely encoded using neuron activities and stored dictionary elements. The reconstructed input can be obtained by performing a backward pass through the same crossbar matrix using the neuron activity vector as input. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals. Using the sparse coding algorithm, natural image processing is performed based on a learned dictionary. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11742901-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2022029665-A1 |
| priorityDate |
2017-04-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |