About: Towards artificial general intelligence with hybrid Tianjic chip architecture

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scientific article published on 01 August 2019

Attributes	Values
rdf:type	Item
description	wetenschappelijk artikel (nl) artículu científicu espublizáu n'agostu de 2019 (ast) scientific article published on 01 August 2019 (en) im Juli 2019 veröffentlichter wissenschaftlicher Artikel (de) наукова стаття, опублікована 1 серпня 2019 (uk)
publication date	wds:Q66988354-50E926A3-E4CA-4959-A6C1-EF35601871DA
publication date	2019-07-31 00:00:00Z (xsd:dateTime)
language of work or name	wds:Q66988354-D015F8B9-8DEC-462E-BA28-087D081D4E01
language of work or name	English
exact match	wds:Q66988354-90195FB1-1925-4442-B114-A0C4D4BC72FA
exact match	https://scigraph.springernature.com/pub.10.1038/s41586-019-1424-8
cites work	wds:Q66988354-00F3CA64-EBB2-4F89-95D5-9028E5BABFA0 wds:Q66988354-02A73E1B-80B0-4DF7-BD82-3BB494EB3CE8 wds:Q66988354-0B072E08-D4A8-478F-8293-031FE733E914 wds:Q66988354-104FFB35-0626-4A18-AF97-529D8A78FA9E wds:Q66988354-1577D3C1-799F-4A39-9A03-417EFA389DB7 wds:Q66988354-15A61C6D-60D4-48B5-B562-12A42AE08D09 wds:Q66988354-3A90DA2F-6211-4884-97BB-653A7F461B37 wds:Q66988354-3B9D635D-D06D-4ED0-BC02-48B6FE91E85E wds:Q66988354-422F556F-69E2-4BB6-ACCD-CFC240AC2F5D wds:Q66988354-4E62FCD8-A146-4F62-8B35-5B05A80AFB13 wds:Q66988354-5D0F794A-0986-46A8-913E-99783D76AEBC wds:Q66988354-5E6E2EA3-816F-41C2-BD31-E7A48AE3BFEA wds:Q66988354-632F21D5-4F9E-4067-916B-65D9A9F05A7F wds:Q66988354-65B29E92-4088-4F69-89CA-954B9E467221 wds:Q66988354-72187720-B460-4DAD-8AF3-3237D27F0944 wds:Q66988354-77C27A9E-C26C-4C39-BF51-64550BB9E2DF wds:Q66988354-77C6CBD5-816D-4751-97FF-706D09B5F494 wds:Q66988354-83048D35-5338-4AE0-A338-CFF2DB45C63E wds:Q66988354-90940675-3FDB-4EAA-83F3-2E14CC96D6AD wds:Q66988354-A6131C98-68D2-47D5-B743-1D4F45C20A20 wds:Q66988354-A78CB655-C5EC-483F-8847-98F7A8BDFE18 wds:Q66988354-AE6B4C11-744F-451C-BE83-1A5EA5949D8F wds:Q66988354-C2C966DD-34DE-49F4-87B4-16E08F4973AD wds:Q66988354-C7F8C256-D60F-4980-B470-2D37851393B8 wds:Q66988354-D401D843-4E44-49D5-9B78-414C2B863FCF wds:Q66988354-E095F489-B3C2-49E1-8478-76D497D664A9 wds:Q66988354-EB8C08C6-D719-4A69-892B-79D3668476E0 wds:Q66988354-F7A549D7-DAE1-4306-88CF-03EF26D57448
cites work	Simple model of spiking neurons. Neuroscience-Inspired Artificial Intelligence The blue brain project A large-scale model of the functioning brain Dendritic computation Algorithms on Strings, Trees, and Sequences Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations Artificial brains. A million spiking-neuron integrated circuit with a scalable communication network and interface Competitive Hebbian learning through spike-timing-dependent synaptic plasticity Gradient-based learning applied to document recognition The SpiNNaker Project Deep learning Mastering the game of Go with deep neural networks and tree search Resolution of Nested Neuronal Representations Can Be Exponential in the Number of Neurons Deep Residual Learning for Image Recognition ImageNet: A large-scale hierarchical image database A wafer-scale neuromorphic hardware system for large-scale neural modeling Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades Building Machines That Learn and Think Like People Loihi: A Neuromorphic Manycore Processor with On-Chip Learning Toward an Integration of Deep Learning and Neuroscience Random synaptic feedback weights support error backpropagation for deep learning Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks. GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework. Control of synaptic plasticity in deep cortical networks. Using neuroscience to develop artificial intelligence EIE: Efficient Inference Engine on Compressed Deep Neural Network
author name string	wds:Q66988354-03C5A4A7-3380-4584-B0D2-065F8C3E5EA6 wds:Q66988354-1107F9B3-2AAF-4D41-A33A-09D2F3D6DAA6 wds:Q66988354-239EE973-6F82-48FD-9A7C-06D543E230B5 wds:Q66988354-25E7AA84-58CC-45F5-AC48-B7553B953E05 wds:Q66988354-3F7814A5-7BDC-438E-B06D-DD3EEB8B5846 wds:Q66988354-45C44383-EC67-42A6-BDFB-7295CEEC81D5 wds:Q66988354-46A2609A-E127-4EA2-82B8-AA7EA3BA3D04 wds:Q66988354-51336113-8B66-45CA-A793-5ECEF8CE9C5F wds:Q66988354-6A159D95-48B4-4D21-973B-648FD50A6184 wds:Q66988354-71C0ABC2-42AB-4DF4-85F6-08A4C1BD359A wds:Q66988354-747CB664-1960-40B2-AB91-6CEE8D80BACC wds:Q66988354-7E1C1024-57DA-48F3-BCCC-4992854C2D92 wds:Q66988354-8A2949F0-44E6-4C58-8D78-1BD37B71F830 wds:Q66988354-8EC3DF13-17BF-4437-B6A6-E1F5211507E8 wds:Q66988354-909F7AB7-995C-442A-96B8-A95CB920B0EF wds:Q66988354-92A6DC7F-9AE3-4F97-9A9C-A28828D7C11E wds:Q66988354-94AE80DF-DAB2-41BF-9871-B6EDE0387301 wds:Q66988354-BEDF255A-CCFF-4A69-B809-4C6C4F9E7E6E wds:Q66988354-CA678DC2-A74E-4D8E-9C82-64D8C6522CC8 wds:Q66988354-DAED294D-ED8E-4F58-9E82-63B532415EA2 wds:Q66988354-EFDF6D8B-7FC0-48C0-9B4E-76DFEEA36E3E wds:Q66988354-F2921C60-575E-4D9A-BE8A-D456953F772D wds:Q66988354-FBB10B8D-521B-4153-8530-6C243D4B5649 wds:Q66988354-FEA36897-DA6E-4A76-A9EE-46293CE5E3EF
author name string	Yu Wang Wei He Feng Chen Lei Deng Guoqi Li Si Wu Sen Song Rong Zhao

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