Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Timur Ibrayev"'
Publikováno v:
IET Cyber-Physical Systems (2018)
The authors propose a discrete-level memristive memory design for analogue data processing in hardware implementations of hierarchical temporal memory (HTM). In this study, memristors were set to ternary and quaternary states in a sub-cell by applica
Externí odkaz:
https://doaj.org/article/a90337a3b4d4445f864dd92bf3ca92e6
Publikováno v:
Acta Metallurgica Slovaca. 27:23-27
The article contains the research results of obtaining a ferroalloy from a carbon ferrochrome dust containing 30,2% of Сr2O3, 23.4% of SiO2, 32.7% of MgO, 5.0% of FeO, 1.6% of CaO, 4.5%of Al2O3, 2.3%of C, and 0.3 %-others. The studies were carried o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8df77705bcacaef575d321ac8843e2ce
https://doi.org/10.36547/ams.27.1.745
https://doi.org/10.36547/ams.27.1.745
TraNNsformer: Clustered Pruning on Crossbar-Based Architectures for Energy-Efficient Neural Networks
Publikováno v:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 39:2361-2374
Implementation of neuromorphic systems using memristive crossbar array (MCA) has emerged as a promising solution to enable low-power acceleration of neural networks. However, the recent trend to design deep neural networks (DNNs) for achieving human-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1eeab87f2d8d2d61b0d9fcd8f34efc43
https://doi.org/10.1109/tcad.2019.2946820
https://doi.org/10.1109/tcad.2019.2946820
Publikováno v:
DAC
The increasing computational demand of Deep Learning has propelled research in special-purpose inference accelerators based on emerging non-volatile memory (NVM) technologies. Such NVM crossbars promise fast and energy-efficient in-situ Matrix Vector
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8dfc6e1eb160c289dd6b9127301478ab
https://doi.org/10.1109/dac18074.2021.9586202
https://doi.org/10.1109/dac18074.2021.9586202
Publikováno v:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 37:1143-1156
Hierarchical temporal memory (HTM) is a machine learning algorithm inspired by the information processing mechanisms of the human neocortex and consists of a spatial pooler (SP) and temporal memory (TM). In this paper, we develop circuits and systems
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0ded2070d1b60c207737c99f5a9260d0
https://doi.org/10.1109/tcad.2017.2748024
https://doi.org/10.1109/tcad.2017.2748024
Publikováno v:
Journal of Intelligent & Fuzzy Systems. 34:1393-1402
Hierarchical Temporal Memory is a new machine learning algorithm intended to mimic the working principle of neocortex, part of the human brain, which is responsible for learning, classification, and making predictions. Although many works illustrate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f121baab9733651480e9a57515a5bdc4
https://doi.org/10.3233/jifs-169434
https://doi.org/10.3233/jifs-169434
Publikováno v:
IEEE Transactions on Biomedical Circuits and Systems. 11:640-651
Hierarchical Temporal Memory (HTM) is an online machine learning algorithm that emulates the neo-cortex. The development of a scalable on-chip HTM architecture is an open research area. The two core substructures of HTM are spatial pooler and tempora
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fbb9b5189ec4500f1c6bfeaa94185c6d
https://doi.org/10.1109/tbcas.2016.2641983
https://doi.org/10.1109/tbcas.2016.2641983
Publikováno v:
Memristor and Memristive Neural Networks
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f22fd3d12e30f007da151fb4f432e1d9
https://doi.org/10.5772/intechopen.70123
https://doi.org/10.5772/intechopen.70123
Publikováno v:
ICECS
Hierarchical Temporal Memory (HTM) is a neuromorphic algorithm that emulates the working principles of neocortex useful for solving image classification problems. Image pixels are transformed to binary HTM feature vectors following a set of weighted
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c3e3eed976a42c76b21367557ea5098b
https://doi.org/10.1109/icecs.2017.8292021
https://doi.org/10.1109/icecs.2017.8292021
Publikováno v:
Circuits and Systems. :265-273
Amplifiers are essential building blocks of a majority of the mixed signal circuits that are used in the development of cognitive computing architectures. Their implementation and use is challenged by the second order effects that dominate the MOSFET
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::06742f2374798820d6bb2b8195b02ac8
https://doi.org/10.4236/cs.2014.511028
https://doi.org/10.4236/cs.2014.511028