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1. (WO2018200899) GRAPH MATCHING FOR OPTIMIZED DEEP NETWORK PROCESSING
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Pub. No.: WO/2018/200899 International Application No.: PCT/US2018/029699
Publication Date: 01.11.2018 International Filing Date: 27.04.2018
IPC:
G06N 3/04 (2006.01) ,G06N 3/063 (2006.01) ,G06N 5/02 (2006.01) ,G06F 8/41 (2018.01) ,G06F 1/32 (2006.01)
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
N
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3
Computer systems based on biological models
02
using neural network models
04
Architecture, e.g. interconnection topology
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
N
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3
Computer systems based on biological models
02
using neural network models
06
Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
063
using electronic means
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
N
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
5
Computer systems utilizing knowledge based models
02
Knowledge representation
[IPC code unknown for G06F 8/41]
G PHYSICS
06
COMPUTING; CALCULATING; COUNTING
F
ELECTRIC DIGITAL DATA PROCESSING
1
Details not covered by groups G06F3/-G06F13/82
26
Power supply means, e.g. regulation thereof
32
Means for saving power
Applicants:
ADVANCED MICRO DEVICES, INC [US/US]; 2485 Augustine Drive Santa Clara, California 95054, US
Inventors:
BRETERNITZ, Mauricio; US
DAGA, Mayank; US
Agent:
RANKIN, Rory D.; US
Priority Data:
15/498,94327.04.2017US
Title (EN) GRAPH MATCHING FOR OPTIMIZED DEEP NETWORK PROCESSING
(FR) MISE EN CORRESPONDANCE DE GRAPHES POUR UN TRAITEMENT DE RÉSEAU PROFOND OPTIMISÉ
Abstract:
(EN) Systems, apparatuses, and methods for enhanced resolution video and security via machine learning are disclosed. A system is configured to receive a source code representation of a neural network. In one embodiment, the source code representation is a directed acyclic graph (DAG). The system determines if the source code representation includes any of one or more patterns, with each pattern including two or more adjacent layers. The system also identifies, for each pattern, a combined layer with which to replace the detected pattern. If any occurrences of the one or more patterns are detected in the source code representation, the system replaces each pattern with a corresponding combined layer. Additionally, the system generates an optimized representation of the neural network, wherein the optimized representation includes replacements for any detected patterns. The optimized representation can be utilized to generate an executable version of the neural network.
(FR) L'invention concerne des systèmes, des appareils et des procédés d'amélioration de résolution de vidéo et de sécurité par l'intermédiaire d'un apprentissage automatique. Un système est configuré pour recevoir une représentation de code source d'un réseau neuronal. Dans un mode de réalisation, la représentation de code source est un graphe acyclique orienté (DAG). Le système détermine si la représentation de code source comprend l'un quelconque d'un ou de plusieurs motifs, chaque motif comprenant au moins deux couches adjacentes. Le système identifie également, pour chaque motif, une couche combinée permettant de remplacer le motif détecté. Si toutes les occurrences du ou des motifs sont détectées dans la représentation de code source, le système remplace chaque motif par une couche combinée correspondante. De plus, le système génère une représentation optimisée du réseau neuronal, la représentation optimisée comprenant des remplacements pour tous les motifs quelconques détectés. La représentation optimisée peut servir à générer une version exécutable du réseau neuronal.
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Designated States: AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DJ, DK, DM, DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IR, IS, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, SD, SE, SG, SK, SL, SM, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW
African Regional Intellectual Property Organization (ARIPO) (BW, GH, GM, KE, LR, LS, MW, MZ, NA, RW, SD, SL, ST, SZ, TZ, UG, ZM, ZW)
Eurasian Patent Office (AM, AZ, BY, KG, KZ, RU, TJ, TM)
European Patent Office (EPO) (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, MC, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR)
African Intellectual Property Organization (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG)
Publication Language: English (EN)
Filing Language: English (EN)