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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Title: Compute-in-memory with Emerging Non-volatile Memories for Accelerating Deep Neural Networks
Committee:
Dr. Shimeng Yu, ECE, Chair , Advisor
Dr. Sung-Kyu Lim, ECE
Dr. Arijit Raychowdhury, ECE
Dr. Shaolan Li, ECE
Dr. Jae-Sun Seo, Arizona State
Abstract: The objective of this research is to accelerate deep neural networks (DNNs) with emerging non-volatile memories (eNVMs) based computing-in-memory (CIM) architecture. The research first focuses on the inference acceleration and proposes a resistive random access memory (RRAM) based CIM architecture. Two generations of RRAM testchips that monolithically integrate the RRAM memory array and CMOS peripheral circuits are designed and fabricated using commercial embedded RRAM process respectively. This research develops a PyTorch based framework that incorporates the device characteristics into the DNN model to evaluate the impact of the eNVM nonidealities on training/inference accuracy. Furthermore, to overcome the challenges posed by the asymmetric conductance tuning behavior of typical eNVMs, this research proposes a novel 2-transistor-1-FeFET (ferroelectric field effect transistor) based synaptic weight cell that exploits hybrid precision for in-situ training and inference, which achieves near-software classification accuracy on MNIST and CIFAR-10 dataset.
