More Like this

1. RRAM-enabled AI Accelerator Architecture

   https://doi.org/10.1109/IEDM19574.2021.9720543  

   Wang, Xinxin; Wu, Yuting; Lu, Wei D. (December 2021, 2021 IEEE International Electron Devices Meeting (IEDM))
2. Neuromorphic Dendritic Synapse Integrating Gated-RRAM

   https://doi.org/10.1109/MWSCAS57524.2023.10405939  

   Barve, Siddharth; Jha, Rashmi (August 2023, IEEE)
3. Improving the accuracy and robustness of RRAM-based in-memory computing against RRAM hardware noise and adversarial attacks

   https://doi.org/10.1088/1361-6641/ac461f  

   Kiran Cherupally, Sai; Meng, Jian; Siraj Rakin, Adnan; Yin, Shihui; Yeo, Injune; Yu, Shimeng; Fan, Deliang; Seo, Jae-Sun (January 2022, Semiconductor Science and Technology)

   Abstract We present a novel deep neural network (DNN) training scheme and resistive RAM (RRAM) in-memory computing (IMC) hardware evaluation towards achieving high accuracy against RRAM device/array variations and enhanced robustness against adversarial input attacks. We present improved IMC inference accuracy results evaluated on state-of-the-art DNNs including ResNet-18, AlexNet, and VGG with binary, 2-bit, and 4-bit activation/weight precision for the CIFAR-10 dataset. These DNNs are evaluated with measured noise data obtained from three different RRAM-based IMC prototype chips. Across these various DNNs and IMC chip measurements, we show that our proposed hardware noise-aware DNN training consistently improves DNN inference accuracy for actual IMC hardware, up to 8% accuracy improvement for the CIFAR-10 dataset. We also analyze the impact of our proposed noise injection scheme on the adversarial robustness of ResNet-18 DNNs with 1-bit, 2-bit, and 4-bit activation/weight precision. Our results show up to 6% improvement in the robustness to black-box adversarial input attacks. 

   more » « less
4. FPRA: A Fine-grained Parallel RRAM Architecture

   https://doi.org/10.1109/ISLPED52811.2021.9502474  

   Liu, Xiao; Zhou, Minxuan; Ausavarungnirun, Rachata; Eilert, Sean; Akel, Ameen; Rosing, Tajana; Narayanan, Vijaykrishnan; Zhao, Jishen (July 2021, ACM/IEEE International Symposium on Low Power Electronics and Design (ISLPED))

   null (Ed.)
5. Reliable and Robust RRAM-based Neuromorphic Computing

   Zhu, Ying; Zheng, Grace Li; Wang, Tianchen; Li, Bing; Shi, Yiyu; Ho, Tsung-Yi; Schlichtmann, Ulf (January 2020, Great Lakes Symposium on VLSI)