Neuromorphic computing based on Analog ReRAM as low power solution for edge application
| Autor: | Mochida Reiji, Yasushi Gohou, Koji Katayama, Kazuyuki Kouno, Hitoshi Suwa, Takumi Mikawa, Ryutaro Yasuhara, Ono Takashi, Toru Kakiage, Masayoshi Nakayama, Hayata Yuriko |
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| Rok vydání: | 2019 |
| Předmět: |
010302 applied physics
Network architecture Resistive touchscreen Artificial neural network Computer science 02 engineering and technology 021001 nanoscience & nanotechnology Perceptron Chip 01 natural sciences Resistive random-access memory Neuromorphic engineering 0103 physical sciences Electronic engineering Node (circuits) 0210 nano-technology |
| Zdroj: | 2019 IEEE 11th International Memory Workshop (IMW). |
| Popis: | We have developed neuromorphic computing based on Analog ReRAM, Resistive Analog Neuromorphic Device (RAND), as low power solution for edge application. We have proposed perceptron circuit which has resistive elements to store weights as analog resistance and binarizes output from each layer in order to realize large scale integration and keep high accuracy. We have fabricated 180nm test chip by using mass production process and we have demonstrated MNIST recognition and sensor application in flexible network architecture in which several neural networks can be configured at the same time. We present potential of low power solution by scaling down to 40nm node under development and reliability issues to be considered in neural network processors based on analog nonvolatile memories. |
| Databáze: | OpenAIRE |
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