Search Results for author: John Paul Strachan

Found 6 papers, 1 papers with code

Analog Feedback-Controlled Memristor programming Circuit for analog Content Addressable Memory

no code implementations21 Apr 2023 Jiaao Yu, Paul-Philipp Manea, Sara Ameli, Mohammad Hizzani, Amro Eldebiky, John Paul Strachan

With the proposed algorithm, the programming and the verification of a memristor can be performed in a single-direction sequential process.

High-Speed and Energy-Efficient Non-Volatile Silicon Photonic Memory Based on Heterogeneously Integrated Memresonator

no code implementations10 Mar 2023 Bassem Tossoun, Di Liang, Stanley Cheung, Zhuoran Fang, Xia Sheng, John Paul Strachan, Raymond G. Beausoleil

Recently, interest in programmable photonics integrated circuits has grown as a potential hardware framework for deep neural networks, quantum computing, and field programmable arrays (FPGAs).

Experimentally realized memristive memory augmented neural network

no code implementations15 Apr 2022 Ruibin Mao, Bo Wen, Yahui Zhao, Arman Kazemi, Ann Franchesca Laguna, Michael Neimier, X. Sharon Hu, Xia Sheng, Catherine E. Graves, John Paul Strachan, Can Li

Memory augmented neural network has been proposed to achieve the goal, but the memory module has to be stored in an off-chip memory due to its size.

One-Shot Learning

Prospects for Analog Circuits in Deep Networks

no code implementations23 Jun 2021 Shih-Chii Liu, John Paul Strachan, Arindam Basu

Emerging dense non-volatile memory technologies can help to provide on-chip memory and analog circuits can be well suited to implement the needed multiplication-vector operations coupled with in-computing memory approaches.

BIG-bench Machine Learning

Long short-term memory networks in memristor crossbars

1 code implementation30 May 2018 Can Li, Zhongrui Wang, Mingyi Rao, Daniel Belkin, Wenhao Song, Hao Jiang, Peng Yan, Yunning Li, Peng Lin, Miao Hu, Ning Ge, John Paul Strachan, Mark Barnell, Qing Wu, R. Stanley Williams, J. Joshua Yang, Qiangfei Xia

Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence.

Emerging Technologies Applied Physics

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