絞り込み

16636

広告

Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing.

著者 Fuller EJ , Keene ST , Melianas A , Wang Z , Agarwal S , Li Y , Tuchman Y , James CD , Marinella MJ , Yang JJ , Salleo A , Talin AA
Science.2019 Apr 25 ; ():.
この記事をPubMed上で見るPubMedで表示
この記事をGoogle翻訳上で見る Google翻訳で開く

スターを付ける スターを付ける     (3view , 0users)

Full Text Sources

Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional computing through parallel programming and read out of artificial neural network weights in a crossbar memory array. However, selective and linear weight updates and <10 nanoampere read currents are required for learning that surpasses conventional computing efficiency. We introduce an ionic floating-gate memory (IFG) array based upon a polymer redox transistor connected to a conductive-bridge memory (CBM). Selective and linear programming of a transistor array is executed in parallel by overcoming the bridging voltage threshold of the CBMs. Synaptic weight read-out with currents <10 nanoampere is achieved by diluting the conductive polymer in an insulating channel to decrease the conductance. The redox transistors endure >1 billion 'read-write' operations and support >1 megahertz 'read-write' frequencies.
PMID: 31023890 [PubMed - as supplied by publisher]
印刷用ページを開く Endnote用テキストダウンロード