The relentless growth of modern Machine Learning models has spurred the adoption of sparsification techniques to simplify their architectures and reduce the computational demands. Network pruning has demonstrated success in maintaining original network accuracy while shedding significant portions of the original weights. However. leveraging this sparsity efficiently remains challengin... https://www.rxkaitori.com/product-category/t5-tubes/
STuning-DL: Model-Driven Autotuning of Sparse GPU Kernels for Deep Learning
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