Author

Xing-Yu Liu

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#machine learning Jul 2026

FPGN: Redefining Ultra-Fast Programmable Gate-based Neural Acceleration with Differentiable LUTs

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

#small language model Preprint Aug 2026

Not All Attention Heads Contribute to Critical Visual Token Selection: Head-Aware Pruning Matters More

ProViP is proposed, a training-free progressive visual token pruning framework that removes redundant visual tokens based on the embedding similarity of input tokens before reasoning of the LLM backbone, and then prunes tokens during reasoning via head-aware pruning.

Chao-Fang Ma, Lin Jiang, Carol Jingyi Li et al. · 1 citation