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20232026
most citedSNNGX: Securing Spiking Neural Networks with Genetic XOR Encryption on RRAM-based Neuromorphic Accelerator

4 citations · 5 across the 14 of their papers we have counts for

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Showing 2024Show all

8 papers · 1 filter

cs.NE2024

RNC: Efficient RRAM-aware NAS and Compilation for DNNs on Resource-Constrained Edge Devices

Kam Chi Loong, Shihao Han, Sishuo Liu +2

Computing-in-memory (CIM) is an emerging computing paradigm, offering noteworthy potential for accelerating neural networks with high parallelism, low latency, and energy efficienc…

cs.CR2024★ 4 cited

SNNGX: Securing Spiking Neural Networks with Genetic XOR Encryption on RRAM-based Neuromorphic Accelerator

Kwunhang Wong, Songqi Wang, Wei Huang +8

Biologically plausible Spiking Neural Networks (SNNs), characterized by spike sparsity, are growing tremendous attention over intellectual edge devices and critical bio-medical app…

cs.ET2024★ 1 cited

Topology Optimization of Random Memristors for Input-Aware Dynamic SNN

Bo Wang, Shaocong Wang, Ning Lin +12

There is unprecedented development in machine learning, exemplified by recent large language models and world simulators, which are artificial neural networks running on digital co…

cs.AR2024

Dynamic neural network with memristive CIM and CAM for 2D and 3D vision

Yue Zhang, Woyu Zhang, Shaocong Wang +14

The brain is dynamic, associative and efficient. It reconfigures by associating the inputs with past experiences, with fused memory and processing. In contrast, AI models are stati…

cs.CR2024

Older and Wiser: The Marriage of Device Aging and Intellectual Property Protection of Deep Neural Networks

Ning Lin, Shaocong Wang, Yue Zhang +6

Deep neural networks (DNNs), such as the widely-used GPT-3 with billions of parameters, are often kept secret due to high training costs and privacy concerns surrounding the data u…

cs.AR2024

Continuous-Time Digital Twin with Analogue Memristive Neural Ordinary Differential Equation Solver

Hegan Chen, Jichang Yang, Jia Chen +20

Digital twins, the cornerstone of Industry 4.0, replicate real-world entities through computer models, revolutionising fields such as manufacturing management and industrial automa…

cs.ET2024

Efficient and accurate neural field reconstruction using resistive memory

Yifei Yu, Shaocong Wang, Woyu Zhang +16

Human beings construct perception of space by integrating sparse observations into massively interconnected synapses and neurons, offering a superior parallelism and efficiency. Re…

cs.AR2024

Resistive Memory-based Neural Differential Equation Solver for Score-based Diffusion Model

Jichang Yang, Hegan Chen, Jia Chen +19

Human brains image complicated scenes when reading a novel. Replicating this imagination is one of the ultimate goals of AI-Generated Content (AIGC). However, current AIGC methods,…