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Classification with a disordered dopantatom network in silicon 期刊论文
NATURE, 2020, 577 (7790) : 341-+
作者:  Vagnozzi, Ronald J.;  Maillet, Marjorie;  Sargent, Michelle A.;  Khalil, Hadi;  Johansen, Anne Katrine Z.;  Schwanekamp, Jennifer A.;  York, Allen J.;  Huang, Vincent;  Nahrendorf, Matthias;  Sadayappan, Sakthivel;  Molkentin, Jeffery D.
收藏  |  浏览/下载:24/0  |  提交时间:2020/07/03

Classification is an important task at which both biological and artificial neural networks excel(1,2). In machine learning, nonlinear projection into a high-dimensional feature space can make data linearly separable(3,4), simplifying the classification of complex features. Such nonlinear projections are computationally expensive in conventional computers. A promising approach is to exploit physical materials systems that perform this nonlinear projection intrinsically, because of their high computational density(5), inherent parallelism and energy efficiency(6,7). However, existing approaches either rely on the systems'  time dynamics, which requires sequential data processing and therefore hinders parallel computation(5,6,8), or employ large materials systems that are difficult to scale up(7). Here we use a parallel, nanoscale approach inspired by filters in the brain(1) and artificial neural networks(2) to perform nonlinear classification and feature extraction. We exploit the nonlinearity of hopping conduction(9-11) through an electrically tunable network of boron dopant atoms in silicon, reconfiguring the network through artificial evolution to realize different computational functions. We first solve the canonical two-input binary classification problem, realizing all Boolean logic gates(12) up to room temperature, demonstrating nonlinear classification with the nanomaterial system. We then evolve our dopant network to realize feature filters(2) that can perform four-input binary classification on the Modified National Institute of Standards and Technology handwritten digit database. Implementation of our material-based filters substantially improves the classification accuracy over that of a linear classifier directly applied to the original data(13). Our results establish a paradigm of silicon-based electronics for smallfootprint and energy-efficient computation(14).


  
Estimating the learning rate of a technology with multiple variants: The case of carbon storage 期刊论文
ENERGY POLICY, 2018, 121: 498-505
作者:  Upstill, Garrett;  Hall, Peter
收藏  |  浏览/下载:0/0  |  提交时间:2019/04/09
Carbon storage  Technology learning  Negative learning rate  Energy modelling  
Non-constant learning rates in retrospective experience curve analyses and their correlation to deployment programs 期刊论文
ENERGY POLICY, 2017, 107
作者:  Wei, Max;  Smith, Sarah Josephine;  Sohn, Michael D.
收藏  |  浏览/下载:6/0  |  提交时间:2019/04/09
Experience curves  Learning rates  Deployment programs  Technology costs  Technology innovation  
Evaluating relative benefits of different types of R & D for clean energy technologies 期刊论文
ENERGY POLICY, 2017, 107
作者:  Shayegh, Soheil;  Sanchez, Daniel L.;  Caldeira, Ken
收藏  |  浏览/下载:1/0  |  提交时间:2019/04/09
Research and development  Clean energy technology  Curve-shifting  Curve-following  Learning investment  Learning curve  
A reply to "Historical construction costs of global nuclear power reactors" 期刊论文
ENERGY POLICY, 2017, 102
作者:  Koomey, Jonathan;  Hultman, Nathan E.;  Grubler, Arnulf
收藏  |  浏览/下载:4/0  |  提交时间:2019/04/09
Nuclear power  Technology costs  Learning rates