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SATO: spiking neural network acceleration via temporal-oriented dataflow and architecture.

, , , , , , , and . DAC, page 1105-1110. ACM, (2022)

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Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness against Adversarial Attack., , and . CoRR, (2018)Optimize Deep Convolutional Neural Network with Ternarized Weights and High Accuracy., , and . WACV, page 913-921. IEEE, (2019)MRIMA: An MRAM-Based In-Memory Accelerator., , , and . IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 39 (5): 1123-1136 (2020)SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network., , , , , , , , and . CoRR, (2021)Non-structured DNN Weight Pruning Considered Harmful., , , , , , , , , and 3 other author(s). CoRR, (2019)T-BFA: Targeted Bit-Flip Adversarial Weight Attack., , , , , and . CoRR, (2020)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)Energy-Efficient Hybrid-RAM with Hybrid Bit-Serial based VMM Support., , , , , and . ACM Great Lakes Symposium on VLSI, page 347-352. ACM, (2021)Cross-layer Designs against Non-ideal Effects in ReRAM-based Processing-in-Memory System., , , , , and . ISQED, page 1-6. IEEE, (2022)SME: ReRAM-based Sparse-Multiplication-Engine to Squeeze-Out Bit Sparsity of Neural Network., , , , , , , , and . ICCD, page 417-424. IEEE, (2021)