6 papers
Enhancing Robustness of Implicit Neural Representations Against Weight Perturbations
Wenyong Zhou, Yuxin Cheng, Zhengwu Liu +3
Implicit Neural Representations (INRs) encode discrete signals in a continuous manner using neural networks, demonstrating significant value across various multimedia applications.…
Distribution-Aware Hadamard Quantization for Hardware-Efficient Implicit Neural Representations
Wenyong Zhou, Jiachen Ren, Taiqiang Wu +3
Implicit Neural Representations (INRs) encode discrete signals using Multi-Layer Perceptrons (MLPs) with complex activation functions. While INRs achieve superior performance, they…
MINR: Efficient Implicit Neural Representations for Multi-Image Encoding
Wenyong Zhou, Taiqiang Wu, Zhengwu Liu +3
Implicit Neural Representations (INRs) aim to parameterize discrete signals through implicit continuous functions. However, formulating each image with a separate neural network~(t…
Extending Straight-Through Estimation for Robust Neural Networks on Analog CIM Hardware
Yuannuo Feng, Wenyong Zhou, Yuexi Lyu +4
Analog Compute-In-Memory (CIM) architectures promise significant energy efficiency gains for neural network inference, but suffer from complex hardware-induced noise that poses maj…
HPD: Hybrid Projection Decomposition for Robust State Space Models on Analog CIM Hardware
Yuannuo Feng, Wenyong Zhou, Yuexi Lyu +4
State Space Models (SSMs) are efficient alternatives to traditional sequence models, excelling at processing long sequences with lower computational complexity. Their reliance on m…
Exploring Layer-wise Information Effectiveness for Post-Training Quantization in Small Language Models
He Xiao, Qingyao Yang, Dirui Xie +7
Large language models with billions of parameters are often over-provisioned: many layers contribute little unique information yet dominate the memory and energy footprint during i…