KL divergence between a general distribution and a perturbed Gaussian reference remains stable with an optimal sqrt(ε) degradation rate under finite second-moment conditions.
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COMET co-optimizes CNN inference via OBC Schemes A/B on inputs/weights, four LUT techniques, and an im2col-based GEMM core to deliver efficient FPGA deployment with negligible accuracy loss on LeNet-5 and All-CNN-C.
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Optimal Stability of KL Divergence under Gaussian Perturbations
KL divergence between a general distribution and a perturbed Gaussian reference remains stable with an optimal sqrt(ε) degradation rate under finite second-moment conditions.
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COMET: Co-Optimization of a CNN Model using Efficient-Hardware OBC Techniques
COMET co-optimizes CNN inference via OBC Schemes A/B on inputs/weights, four LUT techniques, and an im2col-based GEMM core to deliver efficient FPGA deployment with negligible accuracy loss on LeNet-5 and All-CNN-C.