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Direct-to-Event Spiking Neural Network Transfer
Nhan Trong Luu, Duong Trung Luu, Pham Ngoc Nam +1
Spiking Neural Networks (SNNs) have gained increasing attention due to their potential for low-power computation on neuromorphic hardware. A widely adopted training strategy for SN…
Parameter efficient hybrid spiking-quantum convolutional neural network with surrogate gradient and quantum data-reupload
Luu Trong Nhan, Luu Trung Duong, Pham Ngoc Nam +1
The rapid advancement of artificial intelligence (AI) and deep learning (DL) has catalyzed the emergence of several optimization-driven subfields, notably neuromorphic computing an…
Hybrid Temporal-8-Bit Spike Coding for Spiking Neural Network Surrogate Training
Luu Trong Nhan, Luu Trung Duong, Pham Ngoc Nam +1
Spiking neural networks (SNNs) have emerged as a promising direction in both computational neuroscience and artificial intelligence, offering advantages such as strong biological p…
Accuracy-Robustness Trade Off via Spiking Neural Network Gradient Sparsity Trail
Luu Trong Nhan, Luu Trung Duong, Pham Ngoc Nam +1
Spiking Neural Networks (SNNs) have attracted growing interest in both computational neuroscience and artificial intelligence, primarily due to their inherent energy efficiency and…
Hybrid Layer-Wise ANN-SNN With Surrogate Spike Encoding-Decoding Structure
Nhan T. Luu, Duong T. Luu, Pham Ngoc Nam +1
Spiking Neural Networks (SNNs) have gained significant traction in both computational neuroscience and artificial intelligence for their potential in energy-efficient computing. In…