Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing
Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing
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DOI：
10.1126/science.aaw5581
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发表时间：
2019-05-10
期刊：
影响因子：
56.9
通讯作者：
Talin, A. Alec
中科院分区：
文献类型：
--
作者：
Fuller, Elliot J.;Keene, Scott T.;Talin, A. Alec
神经形态计算机可以通过并行编程和在交叉棒存储器阵列中读取人工神经网络权值来克服传统计算固有的效率瓶颈。然而，选择性和线性权重更新和1兆赫的读写频率。
Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional computing through parallel programming and readout of artificial neural network weights in a crossbar memory array. However, selective and linear weight updates and 1-megahertz write-read frequencies.