{"id":116892,"date":"2019-06-30T16:19:41","date_gmt":"2019-06-30T07:19:41","guid":{"rendered":"http:\/\/175.125.95.178\/ai-in-device\/16892\/"},"modified":"2026-04-09T20:26:51","modified_gmt":"2026-04-09T11:26:51","slug":"16892","status":"publish","type":"ai-in-device","link":"http:\/\/ee.presscat.kr\/en\/ai-in-device\/16892\/","title":{"rendered":"Professor Shin-Hyun Choi\u2019s Research Laboratory [Memristor for AI]"},"content":{"rendered":"

Link: https:\/\/www.shinhyunlab.kaist.ac.kr\/<\/a><\/p>\n

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Memristor-based Network Simulation <\/span><\/b><\/p>\n

using State-of-Art Algorithms <\/span><\/b><\/p>\n<\/p><\/div>\n<\/td>\n<\/tr>\n<\/table>\n

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<\/span><\/span><\/span>\u00a0<\/span>SHAPE \u00a0<\/span>* MERGEFORMAT <\/span><\/span><\/p>\n\n\n
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Memristor-based Network Simulation <\/span><\/b><\/p>\n

using State-of-Art Algorithms <\/span><\/b><\/p>\n<\/p><\/div>\n<\/td>\n<\/tr>\n<\/table>\n

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<\/span><\/span>\u25cf<\/strong> Memristor for AI<\/strong><\/p>\n

Memristor, also called RRAMs, have attracted tremendous attention as a candidate for machine learning, neuromorphic computing and artificial intelligence. Memristor has two terminals structure, which allows the device to be fabricated into large crossbar array. Moreover, a single memristor has an analog switching behavior unlike conventional devices such as CMOS based processor. Due to these characteristics, effective matrix operation is possible through memristor array, which makes the memristor adequate as a device for deep learning process and artificial intelligence. The inherent memory effect of memristor removes bottlenecks between memory and processor unit, existing on conventional AI processor. Other properties such as high scalability, low power consumption and fast switching speed are the remarkable strength of memristor for AI and deep learning applications.<\/p>\n

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\u25cf<\/strong> Research area of Emerging Nano Technology and Integrated Systems Lab (ENTIS)<\/strong><\/p>\n

Our lab focuses are 1) to overcome the limitations of conventional memristor and 2) to develop memristor-based platform for various deep neural network(DNN), spiking neural network(SNN) and other applications.<\/p>\n

1. Memristor Devices Development<\/strong><\/p>\n

Conventional memristors suffer from unavoidable spatial-temporal variation due to uncontrollable, stochastic filament formation. Our Lab is now developing a new strategy to achieve uniform switching through CMOS compatible materials\/fabrication steps as well as linearity, retention and endurance.<\/p>\n

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2. Artificial Neural Network Simulation using memristor<\/strong><\/p>\n

To optimize Memristor devices for Artificial Neural Network (ANN) algorithm such as Deep Neural Network (DNN) and Spiking Neural Network (SNN), our Lab is simulating memristor devices arrays using software reflecting hardware conditions.<\/p>\n

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3. Artificial Neural Network System Design and Integration<\/strong><\/p>\n

Our lab designs artificial neural network system on customized PCB board and integrated chip based on memristor device utilized as an AI hardware. The goal is developing large-scale neural network array for AI hardware processing big data. Another aim is integration of the system, broadening the application of memristor-based ANN system.<\/p>\n

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