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Multi-layer LookAhead Kernel Pruning (LAKP-ML)

Updated 10 July 2026
  • The paper introduces LAKP-ML, a structured pruning framework that leverages multi-layer lookahead scores to remove unimportant convolution kernels.
  • The method computes kernel importance using a product of L1 norms across connected layers, offering a global, path-aware evaluation over multiple layers.
  • Empirical results show that LAKP-ML achieves substantial compression (up to 85%) with minimal resolution loss and only slight contrast degradation, enabling efficient FPGA deployment.

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