3 papers
cs.NE2026
Memristor-Based Spiking Neural Network Accelerator for Bio-inspired Interception Task
Qianhou Qu, Sheng Lu, Liuting Shang +4
Spiking neural networks (SNNs) provide event-driven and low-power computation inspired by biological neural systems, but current implementations rely on von Neumann graphics proces…
cs.ET2026
Compact and Energy-Efficient Memristive Spiking Neuromorphic Accelerator for Bio-inspired Interception Tasks
Qianhou Qu, Sheng Lu, Sungyong Jung +2
Spiking neural networks (SNNs) provide an efficient event-driven computing paradigm for bio-inspired interception tasks. However, most implementations rely on von Neumann digital c…
cs.AR2026
An Energy-Efficient RFET-Based Stochastic Computing Neural Network Accelerator
Sheng Lu, Qianhou Qu, Sungyong Jung +2
Stochastic computing (SC) offers significant reductions in hardware complexity for traditional convolutional neural networks (CNNs). However, despite its advantages, stochastic com…