Author

Shang-Kun Li

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Preprint Sep 2026

CMD: An Integrated CGRA Framework with Cluster-Based Distributed Memory Design

CMD, an integrated CGRA framework featuring cluster-based distributed memory design with a co-designed compilation toolchain, is proposed, which includes a novel memory-aware mapper and a design space exploration mechanism that identifies the optimal memory architecture design for specific kernels.

Shang-Kun Li, Cheng Tan, Ze-Yu Li et al. · 0 citations

#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