FP16 quantization preserves accuracy in BEV-based LiDAR place recognition at lower cost while INT8 degradation depends on the network architecture.
Shufflenet: An extremely effi- cient convolutional neural network for mobile devices
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
Warp-tiled CUDA kernel for depthwise convolution delivers 3.26x runtime reduction versus naive baseline and 1.29x end-to-end training speedup using counter-free analysis in cloud settings.
citing papers explorer
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EdgeLPR: On the Deep Neural Network trade-off between Precision and Performance in LiDAR Place Recognition
FP16 quantization preserves accuracy in BEV-based LiDAR place recognition at lower cost while INT8 degradation depends on the network architecture.
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CUDA Kernel Optimization and Counter-Free Performance Analysis for Depthwise Convolution in Cloud Environments
Warp-tiled CUDA kernel for depthwise convolution delivers 3.26x runtime reduction versus naive baseline and 1.29x end-to-end training speedup using counter-free analysis in cloud settings.