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Found 5 papers, 0 papers with code
Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs
We evaluate higher level libraries, which analyze the input characteristics of a convolutional layer, based on which they produce optimized OpenCL (Arm Compute Library and TVM) and CUDA (cuDNN) code.
TASO: Time and Space Optimization for Memory-Constrained DNN Inference
We optimize the trade-off between execution time and memory consumption by: 1) attempting to minimize execution time across the whole network by selecting data layouts and primitive operations to implement each layer; and 2) allocating an appropriate workspace that reflects the upper bound of memory footprint per layer.
Exploiting Weight Redundancy in CNNs: Beyond Pruning and Quantization
Pruning and quantization are proven methods for improving the performance and storage efficiency of convolutional neural networks (CNNs).
Building a Chinese AMR Bank with Concept and Relation Alignments
One significant change we have made to the AMR annotation methodology is the inclusion of the alignment between word tokens in the sentence and the concepts/relations in the CAMR annotation to make it easier for automatic parsers to model the correspondence between a sentence and its meaning representation.
