This paper proposes FPGN, an end-to-end physically-aware framework that closes the gap between LUT-native learning and latency-optimized FPGA implementation, and addresses challenges through a hardware-aligned differentiable formulation for training FPGA-native LUT neurons.
Jia-Wei Liang, Haotong Qin, Lin-Feng Du et al.· arXiv.org· 1 citation
The CVPR 2026@AdvML Workshop Challenge on adversarial multimodal attacks against autonomous-driving VLAs is presented, providing a practical reference for future robustness evaluation and defense design in multimodal autonomous-driving systems.
Tian-Yuan Zhang, Zonglei Jing, Jiangfan Liu et al.· arXiv.org· 0 citations
PTQ4SNN is proposed, a membrane-aware post-training quantization framework that jointly quantizes weights and recurrent membrane states using only a small calibration set and effectively preserves model accuracy under W4 quantization and approximately 4-bit membrane precision.
Hui Xie, Tong Shi, Haotong Qin et al.· 0 citations
Hardware-agnostic strategies for accelerating text-to-image diffusion, such as timestep distillation and feature caching, can reduce inference time without custom kernels or system-level optimization. Among them, multi-resolution generation strategies have recently received broad attention, attaining more than 5x speed...
Xingyu Zheng, Xianglong Liu, Yifu Ding et al.· 1 citation
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