A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Flexible Neural Network Realized by the Probabilistic SiOx Memristive Synaptic Array for Energy‐Efficient Image Learning
The human brain's neural networks are sparsely connected via tunable and probabilistic synapses, which may be essential for performing energy‐efficient cognitive and intellectual functions. In this sense, the implementation of a flexible neural network with probabilistic synapses is a first step toward realizing the ultimate energy‐efficient computing framework. Here, inspired by the efficient threshold‐tunable and probabilistic rod‐to‐rod bipolar synapses in the human visual system, a 16 × 16 crossbar array comprising the vertical form of gate‐tunable probabilistic SiOx memristive synaptic barristor utilizing the Si/graphene heterojunction is designed and fabricated. Controllable stochastic switching dynamics in this array are achieved via various input voltage pulse schemes. In particular, the threshold tunability via electrostatic gating enables the efficient in situ alteration of the probabilistic switching activation (PAct) from 0 to 1.0, and can even modulate the degree of the PAct change. A drop‐connected algorithm based on the PAct is constructed and used to successfully classify the shapes of several fashion items. The suggested approach can decrease the learning energy by up to ≈2,116 times relative to that of the conventional all‐to‐all connected network while exhibiting a high recognition accuracy of ≈93 %.
Flexible Neural Network Realized by the Probabilistic SiOx Memristive Synaptic Array for Energy‐Efficient Image Learning
The human brain's neural networks are sparsely connected via tunable and probabilistic synapses, which may be essential for performing energy‐efficient cognitive and intellectual functions. In this sense, the implementation of a flexible neural network with probabilistic synapses is a first step toward realizing the ultimate energy‐efficient computing framework. Here, inspired by the efficient threshold‐tunable and probabilistic rod‐to‐rod bipolar synapses in the human visual system, a 16 × 16 crossbar array comprising the vertical form of gate‐tunable probabilistic SiOx memristive synaptic barristor utilizing the Si/graphene heterojunction is designed and fabricated. Controllable stochastic switching dynamics in this array are achieved via various input voltage pulse schemes. In particular, the threshold tunability via electrostatic gating enables the efficient in situ alteration of the probabilistic switching activation (PAct) from 0 to 1.0, and can even modulate the degree of the PAct change. A drop‐connected algorithm based on the PAct is constructed and used to successfully classify the shapes of several fashion items. The suggested approach can decrease the learning energy by up to ≈2,116 times relative to that of the conventional all‐to‐all connected network while exhibiting a high recognition accuracy of ≈93 %.
Flexible Neural Network Realized by the Probabilistic SiOx Memristive Synaptic Array for Energy‐Efficient Image Learning
Choi, Sanghyeon (author) / Jang, Jingon (author) / Kim, Min Seob (author) / Kim, Nam Dong (author) / Kwag, Jeehyun (author) / Wang, Gunuk (author)
Advanced Science ; 9
2022-04-01
10 pages
Article (Journal)
Electronic Resource
English
Si nanocrystal-containing SiOx (x<2) produced by thermal annealing of PECVD realized thin films
| British Library Online Contents | 2005
Efficient grouping approach for fault tolerant weight mapping in memristive crossbar array
| DOAJ | 2023
| British Library Online Contents | 2009
A subranging nonuniform sampling memristive neural network-based analog-to-digital converter
| DOAJ | 2023