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MRIMA: An MRAM-Based In-Memory Accelerator.

, , , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 39 (5): 1123-1136 (2020)

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Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack., , and . CoRR, (2018)MRIMA: An MRAM-Based In-Memory Accelerator., , , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 39 (5): 1123-1136 (2020)Optimize Deep Convolutional Neural Network with Ternarized Weights and High Accuracy., , and . WACV, page 913-921. IEEE, (2019)SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network., , , , , , , , and . CoRR, (2021)T-BFA: Targeted Bit-Flip Adversarial Weight Attack., , , , , and . CoRR, (2020)Non-structured DNN Weight Pruning Considered Harmful., , , , , , , , , and 3 other author(s). CoRR, (2019)Efficient and Secure Deep Learning Inference System: A Software and Hardware Co-design Perspective.. Arizona State University, Tempe, USA, (2020)base-search.net (ftarizonastateun:item:62825).Non-Structured DNN Weight Pruning - Is It Beneficial in Any Platform?, , , , , , , , , and 3 other author(s). IEEE Trans. Neural Networks Learn. Syst., 33 (9): 4930-4944 (2022)ReRAM-Sharing: Fine-Grained Weight Sharing for ReRAM-Based Deep Neural Network Accelerator., , , , and . ISCAS, page 1-5. IEEE, (2021)SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network., , , , , , , , and . ICCD, page 417-424. IEEE, (2021)