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• W2977732425 abstract "Spike-timing-dependent-plasticity (STDP) is an unsupervised learning algorithm for spiking neural network (SNN), which promises to achieve deeper understanding of human brain and more powerful artificial intelligence. While conventional computing system fails to simulate SNN efficiently, process-inmemory (PIM) based on devices such as ReRAM can be used in designing fast and efficient STDP based SNN accelerators, as it operates in high resemblance with biological neural network. However, the real-life implementation of such design still suffers from impact of input noise and device variation. In this work, we present a novel stochastic STDP algorithm that uses spiking frequency information to dynamically adjust synaptic behavior. The algorithm is tested in pattern recognition task with noisy input and shows accuracy improvement over deterministic STDP. In addition, we show that the new algorithm can be used for designing a robust ReRAM based SNN accelerator that has strong resilience to device variation." @default.
• W2977732425 created "2019-10-10" @default.
• W2977732425 creator A5009591041 @default.
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• W2977732425 date "2019-07-01" @default.
• W2977732425 modified "2023-09-24" @default.
• W2977732425 title "Improving Robustness of ReRAM-based Spiking Neural Network Accelerator with Stochastic Spike-timing-dependent-plasticity" @default.
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• W2977732425 doi "https://doi.org/10.1109/ijcnn.2019.8851825" @default.
• W2977732425 hasPublicationYear "2019" @default.
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