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- research-articleDecember 2024
A Cascaded ReRAM-based Crossbar Architecture for Transformer Neural Network Acceleration
ACM Transactions on Design Automation of Electronic Systems (TODAES), Volume 30, Issue 1Article No.: 10, Pages 1–23https://doi.org/10.1145/3701034Emerging resistive random-access memory (ReRAM) based processing-in-memory (PIM) accelerators have been increasingly explored in recent years because they can efficiently perform in-situ matrix-vector multiplication (MVM) operations involved in a wide ...
- research-articleSeptember 2024
ReHarvest: An ADC Resource-Harvesting Crossbar Architecture for ReRAM-Based DNN Accelerators
- Jiahong Xu,
- Haikun Liu,
- Zhuohui Duan,
- Xiaofei Liao,
- Hai Jin,
- Xiaokang Yang,
- Huize Li,
- Cong Liu,
- Fubing Mao,
- Yu Zhang
ACM Transactions on Architecture and Code Optimization (TACO), Volume 21, Issue 3Article No.: 63, Pages 1–26https://doi.org/10.1145/3659208ReRAM-based Processing-In-Memory (PIM) architectures have been increasingly explored to accelerate various Deep Neural Network (DNN) applications because they can achieve extremely high performance and energy-efficiency for in-situ analog Matrix-Vector ...
- research-articleAugust 2022
ReGNN: a ReRAM-based heterogeneous architecture for general graph neural networks
DAC '22: Proceedings of the 59th ACM/IEEE Design Automation ConferencePages 469–474https://doi.org/10.1145/3489517.3530479Graph Neural Networks (GNNs) have both graph processing and neural network computational features. Traditional graph accelerators and NN accelerators cannot meet these dual characteristics of GNN applications simultaneously. In this work, we propose a ...
